{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from NYupper 4-28-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST__, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST__:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST__)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  NY\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use ny_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. NY has 20201249 peeps and is divided into 14191 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 27 26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 62 counties in NY .  I will assign them county Nos 0 - 61\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 27 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ab3bbfb-04d6-4d30-bfe3-e74c8a4ecd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For NYlower, need to zoom in on these before finalizing skipList\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For NYlower, need to zoom in on these before finalizing skipList\")\n",
    "for t in skipList:\n",
    "    if tractCP[t].y < 41 and countyNo[t] != 51:  #skip LI\n",
    "        plotPoly(tractGeom[t])\n",
    "        plotCenter(t,tractGeom[t],8)\n",
    "#plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c] and c != 51:\n",
    "        if countyGeom[c].centroid.y < 41.3:\n",
    "            plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 27 units will be axed\n",
      "here is the proposed skipList [7183, 12318, 12569, 13043, 5399, 5400, 4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004, 11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]\n",
      "here is the final skipList [7183, 12318, 13043, 4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004, 11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)  #NYlower - preserve 5399, 5400, 12569\n",
    "skipList = [7183, 12318,  13043,  4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004,\n",
    "            11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]  #12569\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 2\n",
      "removing coastal units from countyNo 23\n",
      "removing coastal units from countyNo 27\n",
      "removing coastal units from countyNo 29\n",
      "removing coastal units from countyNo 30\n",
      "removing coastal units from countyNo 40\n",
      "removing coastal units from countyNo 42\n",
      "removing coastal units from countyNo 51\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.73322\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 286 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 292 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 293 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 294 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3112 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1771 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 1772 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fx0uPw1RngizJGNx9MAL1ga4ui4jcyA2N2FyxYgUkScKiRYvsy4qKijB37lxERkbC398fQ4cOxUcffdTqvl599VX7F/2oUaNw6NChGynNYymKgvPnz+Ps2bOIjo5GcnIyw48HYQByHSEEvr38LfZe2It9hfvQM6gnxsWOw9iYsQw/RNRIu3uA0tPTsWrVKgwaNMhh+bx581BeXo6tW7ciPDwcGzZswOzZs3H48GEMGTKkyX299957WLx4MVauXIlRo0bh5ZdfxtSpU5GVlYWIiIj2luhRru/x6dWrFwwGg6tLIifVKXWosda4ugxVEUIg62oWSqpLAAD9wvqhf3h/F1dFRJ6gXT1AlZWVmDNnDlavXo3Q0FCHdfv378cTTzyBkSNHonfv3vjd736HkJAQZGRkNLu/v//973jooYcwf/589OvXDytXroSfnx/Wrl3bnvI8SsM8PllZWYiKikJycjLDj4fSSlr4aTm2pCtkX83GnoI9+KrwK4QYQjAudhzGxY5DuG+4q0sjIg/RrgC0YMECTJ8+HZMnT260Li0tDe+99x7KysqgKAo2btyI2tpaTJgwocl91dXVISMjw2Ffsixj8uTJOHDgQJOPMZvNMJlMDjdPdOHCBZw5cwbh4eFISUnhAGcPJwRngu5MeaY87CnYgz0Fe2DQGjA+bjzGxY5DpH+kq0sjIg/k9CGwjRs34siRI0hPT29y/fvvv4977rkHYWFh0Gq18PPzw6ZNm5CYmNjk9pcvX4bNZkOPHj0clvfo0QNnzpxp8jHLly/HsmXLnC3dLVx/qCs6OhqxsbGuLok6SIhfCL69+K2ry/AaQgicLjuN0upSAEBcUBzGx413cVVE5C2cCkAFBQVYuHAhduzY0ezA3GeeeQbl5eX44osvEB4ejs2bN2P27Nn46quvMHDgwA4peunSpVi8eLH9vslkQlxcXIfsu7M0BB+LxYKePXvyMJcXigiOgFWxuroMj1djrcHhosOwCRtSuqWgX1g/V5dERF7IqQCUkZGBkpISDB061L7MZrNh7969eOWVV5CVlYVXXnkFJ0+eRP/+9QMRBw8ejK+++gqvvvoqVq5c2Wif4eHh0Gg0KC4udlheXFyMyMimu7YNBoPHBAhFUZCTkwOLxcLBzUQtKKwsxNmys/DR+mB09GjoZJ2rSyIiL+ZUAJo0aRJOnDjhsGz+/PlITk7GkiVLUF1dDaB+DM/1NBpNsxeK1Ov1GDZsGHbu3Ik777wTQH1o2LlzJx5//HFnynMr7PFRJ0XwgqjOEELg+OXjuFp7FVH+UZgYP9HVJRGRSjgVgAIDAzFgwACHZf7+/ggLC8OAAQNgsViQmJiIX/ziF/jb3/6GsLAwbN68GTt27MDHH39sf8ykSZMwc+ZMe8BZvHgx7rvvPgwfPhwjR47Eyy+/jKqqKsyfP78Dmti1Gnp8rFYrgw9RM6ot1ThcfBiKUDAgfAAGdx/s6pKISGU6dCZonU6Hbdu24de//jVuv/12VFZWIjExEevXr8ett95q3+78+fO4fPmy/f4999yD0tJS/P73v0dRURFSU1Oxffv2RgOj3RkPdRHAq8G3JteYixxjDnx1vkiLToNW5mT0ROQakvCCCxiZTCYEBwfDaDQiKCioQ/dtNBpx7tw5xMTEIDIystFMv+zxoeu9d+g93DPyHleX4VasihVHio+gylKFhOAE9A7u7eqSiMhNdOb3d2v451cbxMfHw8fHB+fOnQMA+Pj4IDo6Gnl5eQw+RM24XHMZJ0pPQCNrMKzHMPjr/F1dEhGRHQNQKyRJghACQUFB9nRaU1OD3NxcxMXFMfgQXUcIgZOXT+JyzWWE+YZhQtwEXh+NiNwSA1ArJElqdAabr69vsxM7EqnRDwc1D+zeMXN+ERF1FgagVsiyDC8YJkVdRG2DoL8r/w55pjz46fw4qJmIPAp/W7Wi4RAYEdWzKBYcLjoMs82MXsG9OHcPEXkkBqBWMAAR1SuuKsbpstPQyloM6zEMvlpevJeIPBcDUCuaGgNEpBZCCBwrPQaj2Yjuft0xPnY8BzUTkVdgAGoFxwCRGpnqTDhafBQCAoO7D0aoT6irSyIi6lAMQK3gITBSk7NXz+Ji5UUE6AJwc+zNqhvUTUTqwQDUCgYgcoYnXgzVqliRXpSOWmst+oT2wYS4Ca4uiYio0zEAtYJjgMgZntRjUlZbhmMlx6CRNRjeYzj8dH6uLomIqMswALWCY4DImwghcKrsFEqrSxHqE8qZmolItRiAWsEvB/IGtdZapBelwyZsSOmWgv5h/V1dEhGRSzEAEXmxXGMucow5MGgNGB01GjqNztUlERG5BQYgIi9jUSzIKM6A2WpGXFAcZ2omImoCAxCRlyiqKkJWWRY0sgZDI4ZyUDMRUQsYgIg8mCIUHCs9BpPZhAi/CIyPG+/qkoiIPAIDEJEHMpqNyCzJBACkRqQi2BDs2oKIiDwMAxCRBzlTdgaXKi8h2BDMmZqJiG4AAxCRm6u11uJw8WFYFSuSQpOQHJ/s6pKIiDweAxCRmyqoKMD58vPQy3qMjBwJvUbv6pKIiLwGAxCRG1GEgiPFR1BpqURMQAyvy0VE1EkYgIg6UHsvhlptqcbh4sMQQnBQMxFRF2AAIupAzg5KvlBxAeeunoOvzhdp0WnQyvxIEhF1Bf62JepiDTM111prER0QzZmaiYhcgAGIqItcrLyIs1fPQitrOVMzEZGLMQC1gRDC1SWQh7IpNhwpOYIqSxWi/KM4qJmIyE0wALWBJEmuLoE8zJWaKzhx+QRkSUZqRCqC9EGuLomIiK7DAETUQYQQKKwuxJ6CPQjxCcH42PEMz0REbooBiOgG2Ww25Obmwmq14r4h9yE0KNTVJRERUSsYgIjaqaamBvn5+ZBlGT179oROp3N1SURE1EYMQG3AQdB0vdLSUpSVlcHHxwd9+vSBLPOCpEREnoYBqA04joMURUFeXh7MZjPCw8ORlJTk6pKIiOgGMAARtaC6uhoFBQWQJAnx8fHw8fFxdUlERNQBGICImlBUVASj0cjDXEREXooBiOgaq9VqP5srMjISkZGRri6JiIg6CQMQqV5VVRUKCgqg0Wh4NhcRkUowAJFqXbx4ESaTCf7+/khKSuJgdyIiFWEAIlWxWCzIy8uDzWZDZGQkoqOjXV0SERG5AAMQqUJ5eTmKi4vth7m0Wr71iYjUjN8C5LWEECgoKEBNTQ2Cg4M5dw8REdkxAJHXMZvNyMvLgxACsbGx8Pf3d3VJRETkZhiAyGtcvnwZV65cgV6vR2JiIufuISKiZjEAtQGvBea+Gi5RUVdXh7CwMB7mIiKiNmEAagOeHu1+eIkKIiK6EQxA5FEaLlHh6+uLvn37MpwSEVG7MACR27NarcjLy4PVakVERAQPcxER0Q1jACK3ZTKZcPHiRWi1WiQkJPASFURE1GEYgMitWK1W5Ofnw2KxIDAwkJeoICKiTsEARG6htLQUZWVl0Gg0iI+Ph16vd3VJRETkxW5oopQVK1ZAkiQsWrQIAJCbmwtJkpq8ffDBB83up7KyEo8//jhiY2Ph6+uLfv36YeXKlTdSGnkAq9WK8+fPIysrC7IsIykpCYmJiQw/RETU6drdA5Seno5Vq1Zh0KBB9mVxcXG4dOmSw3ZvvPEGXnjhBUybNq3ZfS1evBhffvkl3n77bfTs2ROff/45HnvsMURHR2PGjBntLZHc1PXX5eLYHiIicoV29QBVVlZizpw5WL16NUJDQ+3LNRoNIiMjHW6bNm3C7NmzERAQ0Oz+9u/fj/vuuw8TJkxAz5498fDDD2Pw4ME4dOhQe8ojN6QoCnJzc5GVlYXa2lp7bw/DDxERuUK7AtCCBQswffp0TJ48ucXtMjIykJmZiQceeKDF7dLS0rB161YUFhZCCIFdu3bh7NmzmDJlSpPbm81mmEwmh1tn4kzQ7VdZWYmsrCxkZ2cjMjISSUlJiIyMdHVZRESkck4fAtu4cSOOHDmC9PT0Vrdds2YNUlJSkJaW1uJ2//znP/Hwww8jNjYWWq0Wsixj9erVGDduXJPbL1++HMuWLXO2dOoiQggUFhaiqqoKfn5+nLCQiIjcjlMBqKCgAAsXLsSOHTtavfRATU0NNmzYgGeeeabV/f7zn//EwYMHsXXrViQkJGDv3r1YsGABoqOjm+xlWrp0KRYvXmy/bzKZEBcX50xTnMIv77apra1Ffn4+hBCIiYlBbGysq0siIiJqkiScOL6zefNmzJw5ExqNxr7MZrNBkiTIsgyz2Wxf9+9//xsPPPAACgsL0b1792b3WVNTg+DgYGzatAnTp0+3L3/wwQdx4cIFbN++vdW6TCYTgoODYTQaERQU1NbmtFl2djYSExM7fL/eori4GOXl5TAYDIiPj+dV2ImIqE06+/u7JU71AE2aNAknTpxwWDZ//nwkJydjyZIlDsFozZo1mDFjRovhBwAsFgssFkujL02NRgNFUZwpr9NwDFBjFosFeXl5sNlsvDwFERF5HKcCUGBgIAYMGOCwzN/fH2FhYQ7Ls7OzsXfvXmzbtq3J/SQnJ2P58uWYOXMmgoKCMH78eDz99NPw9fVFQkIC9uzZg7feegt///vf29Ek6kxlZWUoLS2FVqtFz549odVyLk0iIvI8nfLttXbtWsTGxjZ7FldWVhaMRqP9/saNG7F06VLMmTMHZWVlSEhIwJ///Gc88sgjnVGe09Q+BshmsyE/Px91dXUIDQ1lbw8REXk8p8YAuSuOAeocFRUVuHjxIiRJQkJCAgwGg6tLIiIiL+IxY4DUygsyYpsJIXDhwgVUV1cjICCAp7ATEZFXYgBqo/LycgghOvzWkeGiIai1d59CCCiKgtjY2E6dVoCIiMjVGIDaICEhAUaj0eHirrIsN3vhV2duRERE1PUYgNpAr9e3ejo/EREReQ7OWEdERESqwwBEREREqsMARERERKrDAERERESqwwBEREREqsMARERERKrDAERERESqwwBEREREqsMARER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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c47f795-8f36-40d7-aa39-19cdcdc5d4d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE = \"NYlower\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For NYlower my input file says to cut out 1 districts out of 26\n",
      "performing cut no 0 for state NYlower\n",
      "the total proposed cutPop is 8551321.0 . Compare to 776971 avg districtPop.  This is 11.006 *aDP\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = int(trimDF[\"nCutPoly\"][DFrow])\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts),\n",
    "                  \"avg districtPop.  This is\",r3(cutPop*nDistricts/float(statePop)),\"*aDP\" )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a7cd468a-4be5-41aa-9518-5cc164aa7dd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.00597027441224\n"
     ]
    }
   ],
   "source": [
    "aDP = statePop/nDistricts\n",
    "print(np.sum([tractPop[v] for v in allCutVTDs])/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b2308ec-cb4c-4beb-9c0f-5d07ead43e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total proposed cutPop is 0.0 . Compare to 776971 avg districtPop.  This is 0.0 *aDP\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the state and the last proposed cutPoly.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POLYGON ((-81.1 38.718, -71.2 41.688, -71.2 47, -81.1 43.97, -81.1 38.718))\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 48\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28mprint\u001b[39m(cutPoly)\n\u001b[0;32m     47\u001b[0m oldPts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(cutPoly\u001b[38;5;241m.\u001b[39mexterior\u001b[38;5;241m.\u001b[39mcoords)\n\u001b[1;32m---> 48\u001b[0m newPtXs \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mliteral_eval(\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43menter alternate [ x0, x1 ] for the first two points\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m )\n\u001b[0;32m     49\u001b[0m newPtYs \u001b[38;5;241m=\u001b[39m ast\u001b[38;5;241m.\u001b[39mliteral_eval(\u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menter alternate [ y0, y1 ] for the first two points\u001b[39m\u001b[38;5;124m\"\u001b[39m) )\n\u001b[0;32m     50\u001b[0m thisCutPoints[\u001b[38;5;241m0\u001b[39m], thisCutPoints[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m Point(newPtXs[\u001b[38;5;241m0\u001b[39m],newPtYs[\u001b[38;5;241m0\u001b[39m]), Point(newPtXs[\u001b[38;5;241m1\u001b[39m],newPtYs[\u001b[38;5;241m1\u001b[39m])\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniforge3\\lib\\site-packages\\ipykernel\\kernelbase.py:1282\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[1;34m(self, prompt)\u001b[0m\n\u001b[0;32m   1280\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1281\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[1;32m-> 1282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1284\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1285\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\miniforge3\\lib\\site-packages\\ipykernel\\kernelbase.py:1325\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[1;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[0;32m   1322\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m   1323\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[0;32m   1324\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1325\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1326\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m   1327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    }
   ],
   "source": [
    "#5/1/24 temp - cutting a keeper district out of northern NYlower\n",
    "cutNotDone = True        \n",
    "cutList = list()\n",
    "cutXs = [-81.1, -71.2, -71.2, -81.1]\n",
    "cutYs = [38.718, 41.688, 47, 43.97]\n",
    "cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "thisCutPoints = [cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "while cutNotDone:\n",
    "    cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "    cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "    cutList = list()\n",
    "    cutPop = 0.\n",
    "    for c in range(nCounties):\n",
    "        if cutPoly.intersects(countyGeom[c]):\n",
    "            if cutPoly.contains(countyGeom[c]):\n",
    "                for v in countyTractList[c]:\n",
    "                    if v not in allCutVTDs:\n",
    "                        cutList.append(v)\n",
    "                        cutPop += tractPop[v]\n",
    "                #cutList = cutList + countyTractList[c]\n",
    "                #cutPop += countyPop[c]\n",
    "            else:\n",
    "                for v in countyTractList[c]:\n",
    "                    if v not in allCutVTDs: #already in another list\n",
    "                        if cutPoly.contains(tractCP[v]):\n",
    "                            cutList.append(v)\n",
    "                            cutPop += tractPop[v]\n",
    "                        if STATE == \"NC\":\n",
    "                            if t == 1828:\n",
    "                                print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "    print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts),\n",
    "          \"avg districtPop.  This is\",r3(cutPop*nDistricts/float(statePop)),\"*aDP\" )\n",
    "\n",
    "    doIcut = input(\"enter 1 to apply the cut\")\n",
    "    if str(doIcut) == str(1):\n",
    "        cutNotDone = False\n",
    "        allCutLists.append(cutList)\n",
    "        allCutPops.append(cutPop)\n",
    "    else:\n",
    "        print(\"Here is the state and the last proposed cutPoly.\")\n",
    "        plotPoly(cutPoly)\n",
    "        plotPoly(MAP)\n",
    "        plt.show()\n",
    "        print(cutPoly)\n",
    "        oldPts = list(cutPoly.exterior.coords)\n",
    "        newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "        newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "        thisCutPoints[0], thisCutPoints[1] = Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1])\n",
    "\n",
    "allCutKeptVTDs = list()\n",
    "for v in cutList:\n",
    "    allCutKeptVTDs.append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca1ca9ba-b61f-4993-a0aa-d0ff48ce1a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ABOVE IS THE CORRECT POLYGON -- update code with this 5/2/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")  #For NYlower, we have cut out upstate AND a saved White Plains district\n",
    "cutDistrictNo = list()\n",
    "if STATE == \"NYlower\":\n",
    "    runningCutList = allCutKeptVTDs.copy()\n",
    "    cutDistrictNo = [0 for v in allCutKeptVTDs]\n",
    "else:\n",
    "    runningCutList = allCutVTDs.copy()\n",
    "    for v in allCutVTDs:\n",
    "        for i,L in enumerate(allCutLists):\n",
    "            if v in L:\n",
    "                cutDistrictNo.append(i)\n",
    "                break\n",
    "if STATE != \"NYupper\":  #SKIP FOR NY, AS WE WILL DO LOWER SEPARATELY\n",
    "    nbrDF = pd.DataFrame( {\"vtdNo\":runningCutList,\"cutDistrictNo\":cutDistrictNo} )\n",
    "    outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "    outpath = \"2024state_HD_output/\"+outname\n",
    "    nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "74686069-ffb9-47a0-921d-ef1b75e5e7dd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'allCutKeptVTDs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[75], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m nDistricts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m16\u001b[39m  \u001b[38;5;66;03m#special for lower NY\u001b[39;00m\n\u001b[0;32m      2\u001b[0m nCutDistricts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m----> 3\u001b[0m allCutVTDs \u001b[38;5;241m=\u001b[39m allCutVTDs \u001b[38;5;241m+\u001b[39m \u001b[43mallCutKeptVTDs\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'allCutKeptVTDs' is not defined"
     ]
    }
   ],
   "source": [
    "nDistricts = 16  #special for lower NY\n",
    "nCutDistricts = 1\n",
    "allCutVTDs = allCutVTDs + allCutKeptVTDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 20201249.0 we ignore 8551321.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 6702 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 15 rather than original 16\n",
      "Each district will be drawn to house 776661.867 = 1/ 15 of non-cutout state pop 11649928.0 vs original= 20201249.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "here is the NYlower map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b6a192c5-b176-4c22-9748-2f5a34b00e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in uncutCountyList:  #special for NYlower, zoom in on the active map\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "76b4ff52-e97f-40f1-8bc1-ac442d83b946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in uncutCountyList:  #special for NYlower, zoom in on the active map\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(c,countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(c,countyGeom[c], FONTSIZE)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "the max LAT-scaled distance between counties is 0.79344\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 0.79344  1.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  40 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2c122da0-0284-4cfa-83f3-f6156c91fdc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 11649928.0 776661.8666666667\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts, statePop, statePop/nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 776661\n",
      "checking if county 42 with pop 495747.0 and only 1 neighbor is less pop than 194165 vs districtPop 776661\n",
      "county 42 has pop 495747.0 and its only neighbor has pop 2736074.0 vs districtPop 776661.8666666667 . Default = keep un-fused\n",
      "enter 0 to keep 42 as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "0, 1, or 2.  Any other input taken as a zero 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if county 51 with pop 1525920.0 and only 1 neighbor is less pop than 194165 vs districtPop 776661\n",
      "county 51 has pop 1525920.0 and its only neighbor has pop 1395774.0 vs districtPop 776661.8666666667 . Default = keep un-fused\n",
      "enter 0 to keep 51 as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "0, 1, or 2.  Any other input taken as a zero 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if county 59 with pop 52630.0 and 1 or 2 neighbors is less pop than 194165\n",
      "checking if county 42 with pop 495747.0 and 1 or 2 neighbors is less pop than 194165\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [42]\n",
      "1   [59]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 776661.867 194165\n",
      "You may suggest [ [42]  ] \n",
      "let's decide whether to delete, accept, or modify list [59] with pop 52630.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "495747.0 [42]\n",
      "52630.0 [59]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "solitaryCCBlist = list()  #counties we assign early to be corner clusters by themselves\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "            solitaryCCBlist.append(c)\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop : \n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [ [42]  ] \" )\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList and L[0] not in solitaryCCBlist:\n",
    "        #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15533\n",
      "We defined a total of 0 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAPexterior) and (STATE != \"NYlower\" or c != 43):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                if STATE != \"NYlower\" or c != 43:  #special for NYlower - don't unitize 43, we will \n",
    "                    unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "20 fragmented VTDs out of 14191\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 20 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 1444\n",
      "looking for surrounds, now working on vtd 2910\n",
      "looking for surrounds, now working on vtd 4127\n",
      "looking for surrounds, now working on vtd 5892\n",
      "looking for surrounds, now working on vtd 6423\n",
      "looking for surrounds, now working on vtd 6950\n",
      "looking for surrounds, now working on vtd 7530\n",
      "looking for surrounds, now working on vtd 8030\n",
      "looking for surrounds, now working on vtd 9036\n",
      "looking for surrounds, now working on vtd 10640\n",
      "looking for surrounds, now working on vtd 11148\n",
      "looking for surrounds, now working on vtd 11648\n",
      "looking for surrounds, now working on vtd 12148\n",
      "looking for surrounds, now working on vtd 12649\n",
      "looking for surrounds, now working on vtd 13395\n",
      "special for NYlower , I believe that [7354, 7355, 7984, 8311, 8314, 8315, 7379, 7380, 7791, 7792, 7381, 7793, 7418] are surrounded by vtd (fragment) 8436 . Here, let me show you\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the surrounding operation 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After surround checks, we appear to have 7194 building blocks defined. We captured 19 surrounded VTDs\n",
      "working on unit neighbor lists for county 2\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"NYlower\":  #state-specific manual surround enforcement\n",
    "    novelSurroundeds = [ [7354, 7355, 7984, 8311, 8314, 8315, 7379, 7380, 7791, 7792, 7381, 7793, 7418] ] #, [1991, 2015],[9744,9745,9565,9543 ] ]\n",
    "    novelSurrounders = [8436 ] # ,1984, 1827]\n",
    "    for sss, specialSurrounded in enumerate(novelSurroundeds):\n",
    "        specialSurrounder = novelSurrounders[sss]\n",
    "        #c = 18\n",
    "        #print(\"visualizing VTD fragments in county\",c)\n",
    "        #for v in countyFragVTDset[c]:\n",
    "        #    plotPoly(fragVTDgeom[v])\n",
    "        #    plotCenter(v, fragVTDgeom[v])\n",
    "        #plt.show()\n",
    "        #specialSurrounded = ast.literal_eval(input(\"enter the [v1, v2, ...] list of surrounded fragVTDs from above map\"))\n",
    "        #specialSurrounder = int(input(\"enter the fragVTDno that surrounds these\"))\n",
    "        print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "        c = countyNo[parentVTDno[specialSurrounder]]\n",
    "        plotPoly(countyGeom[c], 0.5)\n",
    "        for v in specialSurrounded:\n",
    "            plotPoly(fragVTDgeom[v])\n",
    "            plotCenter(v,fragVTDgeom[v])\n",
    "        plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "        plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "        plt.show()\n",
    "        shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "        if shouldIcombine == 1:\n",
    "            for v in specialSurrounded:\n",
    "                vv = specialSurrounder\n",
    "                if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                    for sNo, s in enumerate(surroundedVTDs):\n",
    "                        if surrounders[sNo] == v:\n",
    "                            surrounders[sNo] = vv\n",
    "                surroundedVTDs.append(v)\n",
    "                surrounders.append(vv)\n",
    "                #nVTDneighbors[vv] -=1  #don't account strictly; trust that the surrounders are not themselves isolated\n",
    "                nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "                fragVTDpop[vv] += fragVTDpop[v]\n",
    "                fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for iii, c in enumerate(uncutCountyList):\n",
    "    if iii%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sketchy unit 3506 has neighbor list [3132]\n"
     ]
    }
   ],
   "source": [
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e79873ab-0b8e-48a9-b4b0-05b3135b9cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 123\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfda5894-5882-4737-9494-e62ecfe794b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE=\"NYlower\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"01May24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4d5fdc3c-6126-4fa8-b484-1d0052c70aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, MD, MN, NC we move in partial counties via clipPoly's before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state NYlower\n",
      "clipPoly 0 intersects [51] counties and a total of 73 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "#starting with MN, we moved the \"option2\" block earlier in the notebook, as we run it before running option 1 (as in other states)\n",
    "print(\"for some states like FL, MD, MN, NC we move in partial counties via clipPoly's before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = int(trimDF[\"nClipPoly\"][DFrow])\n",
    "nCuts  = int(trimDF[\"nCutPoly\"][DFrow])\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "62667c48-6c66-4381-b01d-518af61651bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7b967d22-41fb-454f-ae47-d72d712daa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9715564f-3b60-43e5-9f1f-02f3dcee3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 22  #OPTIONAL: pick a county for visualization if desired\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760bc48e-0236-4ad7-b3b8-d74795009f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7d775e3a-d23d-4385-a856-76eefdfb8683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "776661.8666666667 15\n"
     ]
    }
   ],
   "source": [
    "print(aDP, nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for simple states to use uncut counties 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 1444 is inside the map's convex hull\n",
      "working on confirming HD center 2910 is inside the map's convex hull\n",
      "working on confirming HD center 4127 is inside the map's convex hull\n",
      "working on confirming HD center 5892 is inside the map's convex hull\n",
      "working on confirming HD center 6423 is inside the map's convex hull\n",
      "working on confirming HD center 6950 is inside the map's convex hull\n",
      "working on confirming HD center 7530 is inside the map's convex hull\n",
      "working on confirming HD center 8030 is inside the map's convex hull\n",
      "working on confirming HD center 9036 is inside the map's convex hull\n",
      "working on confirming HD center 10640 is inside the map's convex hull\n",
      "working on confirming HD center 11148 is inside the map's convex hull\n",
      "working on confirming HD center 11648 is inside the map's convex hull\n",
      "working on confirming HD center 12148 is inside the map's convex hull\n",
      "working on confirming HD center 12649 is inside the map's convex hull\n",
      "working on confirming HD center 13395 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "BUFF = 0.1\n",
    "if STATE == \"NYlower\":\n",
    "    BUFF = 0.02\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for simple states to use uncut counties\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(BUFF) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(BUFF))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.3*BUFF))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.1*BUFF))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 1444 out of 14191 . Time = 0\n",
      "working on tract-tract distances for unit 2623 out of 14191 . Time = 148\n",
      "working on tract-tract distances for unit 3715 out of 14191 . Time = 291\n",
      "working on tract-tract distances for unit 4327 out of 14191 . Time = 421\n",
      "working on tract-tract distances for unit 5993 out of 14191 . Time = 548\n",
      "working on tract-tract distances for unit 6423 out of 14191 . Time = 663\n",
      "working on tract-tract distances for unit 6841 out of 14191 . Time = 775\n",
      "working on tract-tract distances for unit 7283 out of 14191 . Time = 873\n",
      "working on tract-tract distances for unit 7730 out of 14191 . Time = 962\n",
      "working on tract-tract distances for unit 8130 out of 14191 . Time = 1043\n",
      "working on tract-tract distances for unit 9036 out of 14191 . Time = 1118\n",
      "working on tract-tract distances for unit 10538 out of 14191 . Time = 1184\n",
      "working on tract-tract distances for unit 10945 out of 14191 . Time = 1239\n",
      "working on tract-tract distances for unit 11348 out of 14191 . Time = 1288\n",
      "working on tract-tract distances for unit 11748 out of 14191 . Time = 1328\n",
      "working on tract-tract distances for unit 12148 out of 14191 . Time = 1360\n",
      "working on tract-tract distances for unit 12549 out of 14191 . Time = 1391\n",
      "working on tract-tract distances for unit 12949 out of 14191 . Time = 1408\n",
      "working on tract-tract distances for unit 13595 out of 14191 . Time = 1418\n",
      "All done; total elapsed  1420 seconds for NYlower with 15 districts to map\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the uuDist histogram 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 1.6\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "doIprint = int(input(\"enter 1 to print the uuDist histogram\"))\n",
    "if doIprint == 1:\n",
    "    plt.hist(uuDist)\n",
    "    plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 1444 out of 14191 . Time = 0\n",
      "working on unit-unit angles for unit 1688 out of 14191 . Time = 37\n",
      "working on unit-unit angles for unit 2623 out of 14191 . Time = 74\n",
      "working on unit-unit angles for unit 3480 out of 14191 . Time = 109\n",
      "working on unit-unit angles for unit 3715 out of 14191 . Time = 144\n",
      "working on unit-unit angles for unit 4127 out of 14191 . Time = 178\n",
      "working on unit-unit angles for unit 4327 out of 14191 . Time = 214\n",
      "working on unit-unit angles for unit 5791 out of 14191 . Time = 245\n",
      "working on unit-unit angles for unit 5993 out of 14191 . Time = 275\n",
      "working on unit-unit angles for unit 6206 out of 14191 . Time = 305\n",
      "working on unit-unit angles for unit 6423 out of 14191 . Time = 335\n",
      "working on unit-unit angles for unit 6637 out of 14191 . Time = 363\n",
      "working on unit-unit angles for unit 6841 out of 14191 . Time = 389\n",
      "working on unit-unit angles for unit 7063 out of 14191 . Time = 415\n",
      "working on unit-unit angles for unit 7283 out of 14191 . Time = 439\n",
      "working on unit-unit angles for unit 7530 out of 14191 . Time = 463\n",
      "working on unit-unit angles for unit 7730 out of 14191 . Time = 486\n",
      "working on unit-unit angles for unit 7930 out of 14191 . Time = 509\n",
      "working on unit-unit angles for unit 8130 out of 14191 . Time = 532\n",
      "working on unit-unit angles for unit 8332 out of 14191 . Time = 551\n",
      "working on unit-unit angles for unit 9036 out of 14191 . Time = 569\n",
      "working on unit-unit angles for unit 10336 out of 14191 . Time = 587\n",
      "working on unit-unit angles for unit 10538 out of 14191 . Time = 603\n",
      "working on unit-unit angles for unit 10743 out of 14191 . Time = 619\n",
      "working on unit-unit angles for unit 10945 out of 14191 . Time = 633\n",
      "working on unit-unit angles for unit 11148 out of 14191 . Time = 647\n",
      "working on unit-unit angles for unit 11348 out of 14191 . Time = 659\n",
      "working on unit-unit angles for unit 11548 out of 14191 . Time = 670\n",
      "working on unit-unit angles for unit 11748 out of 14191 . Time = 680\n",
      "working on unit-unit angles for unit 11948 out of 14191 . Time = 689\n",
      "working on unit-unit angles for unit 12148 out of 14191 . Time = 697\n",
      "working on unit-unit angles for unit 12349 out of 14191 . Time = 705\n",
      "working on unit-unit angles for unit 12549 out of 14191 . Time = 711\n",
      "working on unit-unit angles for unit 12749 out of 14191 . Time = 716\n",
      "working on unit-unit angles for unit 12949 out of 14191 . Time = 720\n",
      "working on unit-unit angles for unit 13395 out of 14191 . Time = 723\n",
      "working on unit-unit angles for unit 13595 out of 14191 . Time = 725\n",
      "working on unit-unit angles for unit 13795 out of 14191 . Time = 726\n",
      "All done with angles; total elapsed seconds = 726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "776661.867 15 20201249.0 11649928.0 15.0 aDP, nD, trueStatePop, statePop, /aDP \n",
      "11649928.0 11649928 0.5766934509841446\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, trueStatePop, statePop, statePop/aDP, \"aDP, nD, trueStatePop, statePop, /aDP \")\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.  For extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.  For extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 1244.484 of targetWedgePop\n",
      "I am working on HD number 1444 of 14191 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 2623 of 14191 HDs.  Elapsed sec = 99\n",
      "I am working on HD number 3715 of 14191 HDs.  Elapsed sec = 198\n",
      "I am working on HD number 4327 of 14191 HDs.  Elapsed sec = 275\n",
      "I am working on HD number 5993 of 14191 HDs.  Elapsed sec = 370\n",
      "I am working on HD number 6423 of 14191 HDs.  Elapsed sec = 431\n",
      "I am working on HD number 6841 of 14191 HDs.  Elapsed sec = 486\n",
      "I am working on HD number 7283 of 14191 HDs.  Elapsed sec = 552\n",
      "I am working on HD number 7730 of 14191 HDs.  Elapsed sec = 629\n",
      "I am working on HD number 8130 of 14191 HDs.  Elapsed sec = 705\n",
      "I am working on HD number 9036 of 14191 HDs.  Elapsed sec = 777\n",
      "I am working on HD number 10538 of 14191 HDs.  Elapsed sec = 877\n",
      "I am working on HD number 10945 of 14191 HDs.  Elapsed sec = 989\n",
      "I am working on HD number 11348 of 14191 HDs.  Elapsed sec = 1094\n",
      "I am working on HD number 11748 of 14191 HDs.  Elapsed sec = 1187\n",
      "I am working on HD number 12148 of 14191 HDs.  Elapsed sec = 1295\n",
      "I am working on HD number 12549 of 14191 HDs.  Elapsed sec = 1386\n",
      "I am working on HD number 12949 of 14191 HDs.  Elapsed sec = 1474\n",
      "I am working on HD number 13595 of 14191 HDs.  Elapsed sec = 1551\n",
      "1596.39 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.57655 0.05209\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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FtGnT6u0rLCzEokWLUFZWhh49euDll1/Gc889J+3PysrCqVOncOrUKdx6662yY4X4rYXitddew5kzZ+Du7o6IiAhs3LgRjz/+uLR/6NChWL9+PV555RW89NJL6N27NzIyMhAVFSXFvPjiiygvL8fMmTOh1+sxbNgwZGZmQqVyraKmmQUlmJ1+BDe3/+gMFZidfoQrWhMRkUtTiLoZCMkYjUao1WoYDAb4+fm19eXYrMYkMOyNHFlLUF0K1Jb32Lfgfi7qSERETsOW5zdrjTmxg8VlDSZBACAAlBgqcLC4zHEXRURE1I4wEXJiF65YV07E2jgiIiJnw0TIiQV3tm48lLVxREREzoaJkBMbHB4ArVqFhkb/KFA7e2xweP0SJ0RERK6AiZATc1MqsHRUJADUS4bM75eOiuRAaSIicllMhJxcYpQWaybGQKOWd39p1CpOnSciIpdnV4kN6lgSo7R4MFKDg8VluHClAsGda7vD2BJERESujomQi3BTKhDXM7CtL4OIiKhdYdcYERERuSwmQkREROSymAgRERGRy2IiRERERC6LiRARERG5LCZCRERE5LI4fZ6oldSYBNduIiJq55gIEbWCzIISLNt+AiWGCmmbVq3C0lGRXM2biKgdYdcYUQvLLCjB7PQjsiQIAHSGCsxOP4LMgpI2ujIiIroZEyGiFlRjEli2/QSEhX3mbcu2n0CNyVIEERE5GhMhohZ0sLisXktQXQJAiaECB4vLHHdRRETUICZCRC3owpWGkyB74oiIqHUxESJqQcGdVS0aR0RErYuJEFELGhweAK1ahYYmyStQO3tscHiAIy+LiIgawESIqAW5KRVYOioSAOolQ+b3S0dFcj0hIqJ2gokQUQtLjNJizcQYaNTy7i+NWoU1E2O4jhARUTvCBRWJWkFilBYPRmq4sjQRUTvHRIiolbgpFYjrGdjWl0FERI1g1xgRERG5LCZCRERE5LKYCBEREZHLYiJERERELsumRKhHjx5QKBT1XnPmzAEAFBUV4ZFHHkHXrl3h5+eHsWPHorS0VHaOsrIyPPHEE/Dz84O/vz+mT5+Oq1evymKOHTuG4cOHQ6VSISwsDCtXrqx3LZs3b0ZERARUKhX69++PXbt2yfYLIbBkyRJotVp4e3sjPj4eJ0+etOV2iYiIyMnZlAgdOnQIJSUl0isrKwsAMGbMGJSXl2PEiBFQKBTIycnB/v37UVVVhVGjRsFkMknneOKJJ/DDDz8gKysLO3bswN69ezFz5kxpv9FoxIgRI9C9e3ccPnwYq1atwquvvoqPP/5Yivn2228xYcIETJ8+Hfn5+UhOTkZycjIKCgqkmJUrV+Ldd99FSkoK8vLy4OPjg4SEBFRUsMYTERER/Zdohrlz54qePXsKk8kkvvrqK6FUKoXBYJD26/V6oVAoRFZWlhBCiBMnTggA4tChQ1LMP//5T6FQKMS5c+eEEEJ8+OGHokuXLqKyslKKWbBggejTp4/0fuzYsSIpKUl2LbGxsWLWrFlCCCFMJpPQaDRi1apVsmvx8vISn3/+udX3ZzAYBADZPREREVH7Zsvz2+4xQlVVVUhPT8e0adOgUChQWVkJhUIBLy8vKUalUkGpVGLfvn0AgNzcXPj7++POO++UYuLj46FUKpGXlyfF3HPPPfD09JRiEhISUFhYiMuXL0sx8fHxsutJSEhAbm4uAKC4uBg6nU4Wo1arERsbK8VYUllZCaPRKHsRERGR87I7EcrIyIBer8fUqVMBAEOGDIGPjw8WLFiAa9euoby8HC+88AJqampQUlICANDpdAgODpadx93dHQEBAdDpdFJMSEiILMb8vqmYuvvrHmcpxpLly5dDrVZLr7CwMKu/DyIiIup47E6EUlNTMXLkSISGhgIAunbtis2bN2P79u3w9fWFWq2GXq9HTEwMlMqOMTlt0aJFMBgM0uvnn39u60siIiKiVmRXiY0zZ84gOzsbW7ZskW0fMWIEioqKcPHiRbi7u8Pf3x8ajQa33XYbAECj0eDChQuyY27cuIGysjJoNBop5uaZZub3TcXU3W/eptVqZTHR0dEN3peXl5esa4+IiIicm11NNWlpaQgODkZSUpLF/UFBQfD390dOTg4uXLiA0aNHAwDi4uKg1+tx+PBhKTYnJwcmkwmxsbFSzN69e1FdXS3FZGVloU+fPujSpYsUs3v3btlnZmVlIS4uDgAQHh4OjUYjizEajcjLy5NiiIiIiGyeNVZTUyO6desmFixYUG/f2rVrRW5urjh16pT4+9//LgICAsT8+fNlMYmJiWLgwIEiLy9P7Nu3T/Tu3VtMmDBB2q/X60VISIiYNGmSKCgoEBs2bBCdOnUSH330kRSzf/9+4e7uLt58803x448/iqVLlwoPDw9x/PhxKWbFihXC399ffPnll+LYsWPi4YcfFuHh4eL69etW3ytnjREREXU8tjy/bU6EvvrqKwFAFBYW1tu3YMECERISIjw8PETv3r3F6tWrhclkksVcunRJTJgwQfj6+go/Pz/x5JNPiitXrshivv/+ezFs2DDh5eUlbrnlFrFixYp6n7Vp0yZx++23C09PT9GvXz+xc+dO2X6TySQWL14sQkJChJeXl3jggQcsXnNjmAgRERF1PLY8vxVCCNGmTVLtmNFohFqthsFggJ+fX1tfDhEREVnBlud3x5jORURERNQKmAgRERGRy2IiRERERC6LiRARERG5LCZCRERE5LLsWlmaOo4ak8DB4jJcuFKB4M4qDA4PgJtS0daXRURE1C4wEXJimQUlWLb9BEoMFdI2rVqFpaMikRilbeRIIiIi18CuMSeVWVCC2elHZEkQAOgMFZidfgSZBSVtdGVERETtBxMhJ1RjEli2/QQsrZRp3rZs+wnUmLiWJhERuTYmQk7oYHFZvZagugSAEkMFDhaXOe6iiIiI2iEmQk7owpWGkyB74oiIiJwVEyEnFNxZ1aJxREREzoqJkBMaHB4ArVqFhibJK1A7e2xweIAjL4uIiKjdYSLkhNyUCiwdFQkA9ZIh8/uloyK5nhAREbk8JkJOKjFKizUTY6BRy7u/NGoV1kyM4TpCRERE4IKKTi0xSosHIzVcWZqIiKgBTIScnJtSgbiegW19GURERO0Su8aIiIjIZbFFyMWxKCsREbkyJkIujEVZiYjI1bFrzEWxKCsRERETIZfEoqxERES1mAi5IBZlJSIiqsVEyAWxKCsREVEtJkIuiEVZiYiIajERckEsykpERFSLiZALYlFWIiKiWkyEXNSDkRrMi78dam8P2XYWZSUiIlfCBRVdkKWFFP29PfDk3T3wzP292RJEREQugy1CLiazoARPW1hI0XC9Gm9nn0TWCV0bXRkREZHjMRFyITUmgYVbjlvcx4UUiYjIFdmUCPXo0QMKhaLea86cOQAAnU6HSZMmQaPRwMfHBzExMfjiiy+k4/fs2WPxeIVCgUOHDgEATp8+bXH/gQMHZNeyefNmREREQKVSoX///ti1a5dsvxACS5YsgVarhbe3N+Lj43Hy5Em7viRn8X7OKeivVTe4nwspEhGRq7EpETp06BBKSkqkV1ZWFgBgzJgxAIDJkyejsLAQ27Ztw/Hjx/Hoo49i7NixyM/PBwAMHTpUdnxJSQmeeuophIeH484775R9VnZ2tixu0KBB0r5vv/0WEyZMwPTp05Gfn4/k5GQkJyejoKBAilm5ciXeffddpKSkIC8vDz4+PkhISEBFhWsuElhjEkjbX2xVLBdSJCIiV6EQQtjdDzJv3jzs2LEDJ0+ehEKhgK+vL9asWYNJkyZJMYGBgXjjjTfw1FNP1Tu+uroat9xyC5599lksXrwYQG2LUHh4OPLz8xEdHW3xc8eNG4fy8nLs2LFD2jZkyBBER0cjJSUFQgiEhobi+eefxwsvvAAAMBgMCAkJwbp16zB+/Hir7s9oNEKtVsNgMMDPz8/ar8WhakwCB4vLcOFKBYI71679Y2mwc27RJUz45ICFM9T3+YwhiOsZ2NKXSkRE5BC2PL/tHiNUVVWF9PR0TJs2DQpF7YN36NCh2LhxI8rKymAymbBhwwZUVFTgvvvus3iObdu24dKlS3jyySfr7Rs9ejSCg4MxbNgwbNu2TbYvNzcX8fHxsm0JCQnIzc0FABQXF0On08li1Go1YmNjpRhLKisrYTQaZa/2LLOgBMPeyMGETw5g7oajmPDJAQx7I8di5XhrW3n8vT24kCIREbkMuxOhjIwM6PV6TJ06Vdq2adMmVFdXIzAwEF5eXpg1axa2bt2KXr16WTxHamoqEhIScOutt0rbfH19sXr1amzevBk7d+7EsGHDkJycLEuGdDodQkJCZOcKCQmBTqeT9pu3NRRjyfLly6FWq6VXWFiYdV9GG8gsKMFsC7O/dIYKzE4/Ui8ZsrZcxpN39+D0eSIichl2ryOUmpqKkSNHIjQ0VNq2ePFi6PV6ZGdnIygoCBkZGRg7diy++eYb9O/fX3b8L7/8gq+++gqbNm2SbQ8KCsL8+fOl93fddRfOnz+PVatWYfTo0fZerlUWLVok+2yj0dguk6Eak8Cy7SdgqU9ToHZ16GXbT+DBSI2U1JjLaugMFRaPA4AunTzwzP29W+mqiYiI2h+7WoTOnDmD7Oxs2bifoqIivP/++1i7di0eeOABDBgwAEuXLsWdd96JDz74oN450tLSEBgYaFVyExsbi1OnTknvNRoNSktLZTGlpaXQaDTSfvO2hmIs8fLygp+fn+zVHh0sLqvXElSXpdlfjZXVMG9b/mh/tgYREZFLsSsRSktLQ3BwMJKSkqRt165dqz2hUn5KNzc3mEwm2TYhBNLS0jB58mR4eMhLPFhy9OhRaLW/lXyIi4vD7t27ZTFZWVmIi4sDAISHh0Oj0chijEYj8vLypJiOzNrxPjfHJUZpsWZiDDRqeTeZlmU1iIjIRdncNWYymZCWloYpU6bA3f23wyMiItCrVy/MmjULb775JgIDA5GRkYGsrCzZ7C4AyMnJQXFxscWZZJ999hk8PT0xcOBAAMCWLVuwdu1afPrpp1LM3Llzce+992L16tVISkrChg0b8N133+Hjjz8GACgUCsybNw+vv/46evfujfDwcCxevBihoaFITk629ZbbHWvH+1iKS4zS4sFIjVUzzYiIiJydzYlQdnY2zp49i2nTpsm2e3h4YNeuXVi4cCFGjRqFq1evolevXvjss8/w0EMPyWJTU1MxdOhQREREWPyM1157DWfOnIG7uzsiIiKwceNGPP7449L+oUOHYv369XjllVfw0ksvoXfv3sjIyEBUVJQU8+KLL6K8vBwzZ86EXq/HsGHDkJmZCZXKuiSiPWtqvI8CtcVTG5r95aZU2DQ93top+kRERB1Ns9YRcnbteR0h86wxALJkyJyetFRXl6UCrVq1CktHRbIrjYiI2iWHrCNEbauh8T6aFhzvY+sUfSIioo7G7unz1PZac7yPPVP0iYiIOhomQh2cNeN97BnjY8sUfZbjICKijoqJkJOzd4yPvVP0iYiIOhKOEXJi1o7xqTEJ5BZdwpdHzyG36BJqTKJZU/SJiIg6CrYIOSlrx/iYTMBrO+u3GC1O6tusKfpEREQdAVuEnJS1Y3z+sN5yi9Gc9fkYPaDhrjMBYPQALQdKExFRh8ZEyEk1Z+yOuQVo2/cleGp4eINxH+8t5hR6IiLq0JgIOanmjt0xtxh9ceSXRuOWbT+BGhPX5CQioo6JiZCTMpfhaG7HVVl5dYP7LFW5JyIi6kiYCDkpN6UCS0dFAkC9ZKilR/VwCj0REXVUTIScWGNlOD78/cBGW4wUAAJ9PK36HE6hJyKijorT551cY2U4lEoFZqcfgQKWC7e+9nAUXtt5glPoiYjIaTERcgENleEwtxjdvPK0ps7K00olGk2Wlo6K5BR6IiLqsBRCCE75aYDRaIRarYbBYICfn19bX06raaoWmb1lOoiIiNqCLc9vJkKNcJVEyBr2FG4lIiJqC7Y8v9k1Rlaxpso9ERFRR8NZY0REROSymAgRERGRy2LXmAvjuB8iInJ1TIRcFGeCERERsWvMJWUWlGB2+hFZEgQAOkMFZqcfYUV5IiJyGUyEXEyNSWDZ9hMWV4o2b2NFeSIichVMhFzMweKyei1BdbGiPBERuRImQi7G2krxrChPRESugIOlnUxTM8GsrRTPivJEROQKmAg5EWtmgg0OD4BWrWJFeSIiIrBrzGlYOxPMTanA0lGRAH6rIG/GivJERORqmAg5AVtngiVGabFmYgw0ann3l0atwpqJMVxHiIiIXAa7xpyALTPBzIVTE6O0eDBSw5WliYjIpdnUItSjRw8oFIp6rzlz5gAAdDodJk2aBI1GAx8fH8TExOCLL75o8hwrVqyQxRw7dgzDhw+HSqVCWFgYVq5cWe9aNm/ejIiICKhUKvTv3x+7du2S7RdCYMmSJdBqtfD29kZ8fDxOnjxpy+12GPbOBDNXlH84+hbE9QxkEkRERC7HpkTo0KFDKCkpkV5ZWVkAgDFjxgAAJk+ejMLCQmzbtg3Hjx/Ho48+irFjxyI/P192nj//+c+y8zz77LPSPqPRiBEjRqB79+44fPgwVq1ahVdffRUff/yxFPPtt99iwoQJmD59OvLz85GcnIzk5GQUFBRIMStXrsS7776LlJQU5OXlwcfHBwkJCaioaPtp4TUmgdyiS/jy6DnkFl1q9uKFnAlGRERkH4UQwu6n8Lx587Bjxw6cPHkSCoUCvr6+WLNmDSZNmiTFBAYG4o033sBTTz0FoLZFaN68eZg3b57Fc65ZswYvv/wydDodPD09AQALFy5ERkYGfvrpJwDAuHHjUF5ejh07dkjHDRkyBNHR0UhJSYEQAqGhoXj++efxwgsvAAAMBgNCQkKwbt06jB8/3qr7MxqNUKvVMBgM8PPzs/n7saQ1anzVmASGvZHT5EywfQvuZ6sPERE5PVue33YPlq6qqkJ6ejqmTZsGhaL24Tp06FBs3LgRZWVlMJlM2LBhAyoqKnDffffJjl2xYgUCAwMxcOBArFq1Cjdu3JD25ebm4p577pGSIABISEhAYWEhLl++LMXEx8fLzpmQkIDc3FwAQHFxMXQ6nSxGrVYjNjZWirGksrISRqNR9mpJrVXjizPBiIiI7GN3IpSRkQG9Xo+pU6dK2zZt2oTq6moEBgbCy8sLs2bNwtatW9GrVy8p5o9//CM2bNiAr7/+GrNmzcJf/vIXvPjii9J+nU6HkJAQ2WeZ3+t0ukZj6u6ve5ylGEuWL18OtVotvcLCwqz9OprU2jW+zDPBQvw4E4yIiMhads8aS01NxciRIxEaGiptW7x4MfR6PbKzsxEUFISMjAyMHTsW33zzDfr37w8AmD9/vhR/xx13wNPTE7NmzcLy5cvh5eXVjFtpvkWLFsmuz2g0tlgyZM/MLvvIE6lm9HwSERE5PbsSoTNnziA7OxtbtmyRthUVFeH9999HQUEB+vXrBwAYMGAAvvnmG3zwwQdISUmxeK7Y2FjcuHEDp0+fRp8+faDRaFBaWiqLMb/XaDTS/7UUU3e/eZtWq5XFREdHN3hfXl5erZaMtXaNL3O3281pT6mxErPTj7BViIiIyAK7usbS0tIQHByMpKQkadu1a9dqT6iUn9LNzQ0mk6nBcx09ehRKpRLBwcEAgLi4OOzduxfV1dVSTFZWFvr06YMuXbpIMbt375adJysrC3FxcQCA8PBwaDQaWYzRaEReXp4U42itObOrtbvdiIiInJXNiZDJZEJaWhqmTJkCd/ffGpQiIiLQq1cvzJo1CwcPHkRRURFWr16NrKwsJCcnA6gd5Pz222/j+++/x3/+8x/84x//wHPPPYeJEydKSc7vf/97eHp6Yvr06fjhhx+wceNGvPPOO7Iuq7lz5yIzMxOrV6/GTz/9hFdffRXfffcdnnnmGQCAQqHAvHnz8Prrr0tT+SdPnozQ0FDpWhzNXOOroeHKCtTOHrOnxpct3W5ERET0G5u7xrKzs3H27FlMmzZNtt3DwwO7du3CwoULMWrUKFy9ehW9evXCZ599hoceeghAbdfThg0b8Oqrr6KyshLh4eF47rnnZEmOWq3Gv/71L8yZMweDBg1CUFAQlixZgpkzZ0oxQ4cOxfr16/HKK6/gpZdeQu/evZGRkYGoqCgp5sUXX0R5eTlmzpwJvV6PYcOGITMzEypV26ylY57ZNTv9CBSQj+Rp7syu1u52IyIiclbNWkfI2XWUdYRyiy5hwicHmoz7fMaQZg7EJiIiav9seX6z1piDtUaNL3O3W1MLKtrT7UZEROTMmAi1AXONr5Y8X1PdbouT+rLAKhER0U2YCDkJ84KKN3e7adQqjB6gxWs7f2zR7jgiIiJnwDFCjWiNMUKtrcYkZC0/l8urMGd9/fWFzG1BXF+IiIicDccIubC63W7mYqwNrS+kQO36Qg9GathNRkRELsnuWmPU/nF9ISIiosYxEXJiXF+IiIiocUyEnFhrlvUgIiJyBkyEnFhrlvUgIiJyBkyEnJh5fSEA9ZKh5pb1ICIicgZMhJyceX0hjVre/aVRqzh1noiIXB6nz7uA1ijrQURE5AyYCLmIli7rQURE5AzYNUZEREQuiy1CTubmEhvsAiMiImoYEyEnkllQUq/oKourEhERNYxdY04is6AEs9OP1CupoTNUYHb6EWQWlLTRlREREbVfTIScQI1JYNn2Ew0WVwVqi6vWmCxFEBERuS4mQk6AxVWJiIjsw0TICbC4KhERkX2YCDkBFlclIiKyDxMhJ8DiqkRERPZhIuQEWFyViIjIPkyEnASLqxIREdmOCyo6ERZXJSIisg1bhDqYGpNAbtElfHn0HHKLLtVbG8hNqcDg8AAEd1bhwpXaKfNcP4iIiMgytgh1INaU0GCZDSIiIusphBBsLmiA0WiEWq2GwWCAn59fm16LuYTGzT+WudNrzcQYAGgyhskQERE5O1ue32wR6gCaKqGhQG0JDSFEkzEPRmo4ZoiIiOi/OEaoA7C2hIbOWNlkDMtsEBER/YaJUAfQkqUxWGaDiIjoNzYlQj169IBCoaj3mjNnDgBAp9Nh0qRJ0Gg08PHxQUxMDL744gvp+NOnT2P69OkIDw+Ht7c3evbsiaVLl6KqqkoWY+kzDhw4ILuWzZs3IyIiAiqVCv3798euXbtk+4UQWLJkCbRaLby9vREfH4+TJ0/a/AW1By1ZGoNlNoiIiH5jUyJ06NAhlJSUSK+srCwAwJgxYwAAkydPRmFhIbZt24bjx4/j0UcfxdixY5Gfnw8A+Omnn2AymfDRRx/hhx9+wFtvvYWUlBS89NJL9T4rOztb9lmDBg2S9n377beYMGECpk+fjvz8fCQnJyM5ORkFBQVSzMqVK/Huu+8iJSUFeXl58PHxQUJCAioqOl6LSFMlNACgqWE/LLNBRERUX7Nmjc2bNw87duzAyZMnoVAo4OvrizVr1mDSpElSTGBgIN544w089dRTFs+xatUqrFmzBv/5z38A1LYIhYeHIz8/H9HR0RaPGTduHMrLy7Fjxw5p25AhQxAdHY2UlBQIIRAaGornn38eL7zwAgDAYDAgJCQE69atw/jx4626v/Y4awyAxQHRjeGsMSIiciW2PL/tHiNUVVWF9PR0TJs2DQpF7aN26NCh2LhxI8rKymAymbBhwwZUVFTgvvvua/A8BoMBAQH1WylGjx6N4OBgDBs2DNu2bZPty83NRXx8vGxbQkICcnNzAQDFxcXQ6XSyGLVajdjYWCnGksrKShiNRtmrvWiohIY1E8BYZoOIiMgyu6fPZ2RkQK/XY+rUqdK2TZs2Ydy4cQgMDIS7uzs6deqErVu3olevXhbPcerUKbz33nt48803pW2+vr5YvXo17r77biiVSnzxxRdITk5GRkYGRo8eDaB2LFJISIjsXCEhIdDpdNJ+87aGYixZvnw5li1bZv2X4GA3l9C4eKUSr+38scnj3nx8AO7uHeSAKyQiIupY7E6EUlNTMXLkSISGhkrbFi9eDL1ej+zsbAQFBSEjIwNjx47FN998g/79+8uOP3fuHBITEzFmzBjMmDFD2h4UFIT58+dL7++66y6cP38eq1atkhKh1rJo0SLZZxuNRoSFhbXqZ96sxiQarRXmplQgrmcgAODLo+esOufF8oan1RMREbkyuxKhM2fOIDs7G1u2bJG2FRUV4f3330dBQQH69esHABgwYAC++eYbfPDBB0hJSZFiz58/j9/97ncYOnQoPv744yY/LzY2VhqYDQAajQalpaWymNLSUmg0Gmm/eZtWq5XFNDTuCAC8vLzg5eXV5PW0FlvLY1g7AyzIp+3uiYiIqD2za4xQWloagoODkZSUJG27du1a7QmV8lO6ubnBZDJJ78+dO4f77rsPgwYNQlpaWr14S44ePSpLaOLi4rB7925ZTFZWFuLi4gAA4eHh0Gg0shij0Yi8vDwppr0xD4a+eeFEnaECs9OPILOgpN4x5tlkTXl+8/cWjyciInJ1NrcImUwmpKWlYcqUKXB3/+3wiIgI9OrVC7NmzcKbb76JwMBAZGRkICsrS5rdZU6CunfvjjfffBO//vqrdLy5Feezzz6Dp6cnBg4cCADYsmUL1q5di08//VSKnTt3Lu69916sXr0aSUlJ2LBhA7777jupdUmhUGDevHl4/fXX0bt3b4SHh2Px4sUIDQ1FcnKy7d9SK7O2hMbN5THclAqMHqDFR3uLGz1/qbE2meKAaSIiIjmbE6Hs7GycPXsW06ZNk2338PDArl27sHDhQowaNQpXr15Fr1698Nlnn+Ghhx4CUNtqc+rUKZw6dQq33nqr7Pi6s/hfe+01nDlzBu7u7oiIiMDGjRvx+OOPS/uHDh2K9evX45VXXsFLL72E3r17IyMjA1FRUVLMiy++iPLycsycORN6vR7Dhg1DZmYmVKq2X1Dw5nFAN2pMVpXQOFhcJo0PMp9n2/dNt/Q0lEw1NR6JiIjI2bH6fCNaYx0hS+OAFLBubaB3xkfj4ehbpPe5RZcw4ZMDjRxR3+czhiCuZ6BV45GYKBERUUfE6vPtlHkc0M1Jj7WZ6M2Do+2pG3bhSkWD12Eej7RmYgwA2DRwm4iIqCNiIuQgjY0DaooCtYsi3lwew566YUG+Xnhh8/eNjkdauOU4DNeqG02UmAwREZEzYPV5BzlYXNboOKCmLB0VWa9banB4APw7eVh1vLnWGASaHI+kt5AEmfcBtS1FNSb2qBIRUcfHRMhB7OnGAgB/b49mt8CY06eloyKbvbhi3YHbREREHR0TIQexpxsLAD54ouEk6GBxGfTXqps8R4CPp5RM2XsdN7M3sSMiImpPmAg5iHnxQ2vnXJm7sobcFthgjLXJyCtJfaVkytbraEhLJVRERERtiYmQg7gpFVg6KhIAmkxC6nZlNTZd3dpkRKP2tngdDfHxdGvwGs0J2s0Dt4mIiDoiJkIOlBilxZqJMdDcVBbj5lxHo1ZZNS6oqdadhpKWxCgtZt4T3uB5y6tqpBlkN58PaDpBIyIi6ig4fd7BEqO0eDBSI1uocFD3Ljh85rLNCxeaW3dmpx+ptyhjY0lLUytSKwCoO3lA5e4GnfG37jcN1xEiIiInw0SoDbgpFbJSGQDqvbeWuZXp5sUPG0tamprKb55C/4/pMVAqFVxZmoiInBYTISdgqZWpsaTF2kHWF8srpZIeLLdBRETOiImQk7DUytQQawdZm+OsqUtGRETUEXGwtJOrMQnkFl3Cl0fPIbfoEmpMwqZB1ua6ZDd3pZnLbWQWNDzWiIiIqL1ji5ATa6wlx5pB1gAarI9mnlW2bPsJPBipabVuMnbJERFRa2Ii5KQaqjBfUqdwalODrHOLLjU5qNpcbsPewd5N3QO75IiIqDUxEXJCTVW6F6htydm34P5GB1lbO6i6NcptNJTI6eokckyGiIiouZgIOSFrKt3Xbcmp25pjHlN04UoFLl6xrkBrS5fbaCyRc1SXHBERuQYmQk5IZ7huVdw3J3+VtQBZ6opSKgBTA01LCtR2pbV0uQ1r1jlqzS45IiJyHUyEOjDzQGKd4TrKyqsQ4OsFjZ8KF69WWXX8h3uKsDX/nDQw2lJXVGNJENA65TbaskuOiIhcCxOhDspS642Z2tvD6vOUGCrwdPoR+Hu7NzimCKjfMtSa5TZsXeeIiIjIXkyEOhBzC1D2CR1S959uMM5wvdrmc+uv32h0v0kAi5P6IqizV6tPYzevc6QzVFhMzlqrS46IiFwPE6E2Yuv6OI21ADlKUGcvqeRGa7K3mCwREZGtmAi1AVvXx2loKrmjnSy9ityiSw5Z1NCeYrJERES2Uggh2vr52m4ZjUao1WoYDAb4+fm1yDkbSmrMacXN6+PUmASGvZHTpi1BN2ssaWuspcueVaK5sjQREdnKluc3W4QcyJ71caxZE8jRGlrUsLGWLgB2rRJtSzFZIiIiW7HoqgPZsj6OWXucIm5O5JZtP4Ga/04la6w469PpR/A0C7cSEVE7xETIgexZH6e9ThE3J22r//UT9p+8iFe3NdzS1dg5AHlCRURE5EhMhBzInvVxzFPJ2+uomA/3/AdPpOZBZ7Sv5cpSKxgREZGjMBFyoKaSGgVqx83UXR/HPJXcvP/meACYNKQbOnm6tfTlOtTNrWXmmmdfHj2H3KJLbDEiIqJWwcHSDlR3fZybmdfLGRlVWw2+7uyoxqaSjx6gxbbvS3CtqsZBd9E66raC2bq8ABERkb1sahHq0aMHFApFvdecOXMAADqdDpMmTYJGo4GPjw9iYmLwxRdfyM5RVlaGJ554An5+fvD398f06dNx9epVWcyxY8cwfPhwqFQqhIWFYeXKlfWuZfPmzYiIiIBKpUL//v2xa9cu2X4hBJYsWQKtVgtvb2/Ex8fj5MmTttxuq1F3slAC47/NO2v3n8aETw5g2Bs5skHEiVFa7FtwPz6fMQTvjI/G5zOGYHFSX3y8t7jdzSqzxc2tYI0NuubAaiIiamk2JUKHDh1CSUmJ9MrKygIAjBkzBgAwefJkFBYWYtu2bTh+/DgeffRRjB07Fvn5+dI5nnjiCfzwww/IysrCjh07sHfvXsycOVPabzQaMWLECHTv3h2HDx/GqlWr8Oqrr+Ljjz+WYr799ltMmDAB06dPR35+PpKTk5GcnIyCggIpZuXKlXj33XeRkpKCvLw8+Pj4ICEhARUVbZc0mB/y+mv1S2DcvJqTpQe/eSr5w9G3YHB4ABZtPd7miyzaoqGuPfMq0U0tLwBwYDUREbWsZi2oOG/ePOzYsQMnT56EQqGAr68v1qxZg0mTJkkxgYGBeOONN/DUU0/hxx9/RGRkJA4dOoQ777wTAJCZmYmHHnoIv/zyC0JDQ7FmzRq8/PLL0Ol08PT0BAAsXLgQGRkZ+OmnnwAA48aNQ3l5OXbs2CF9zpAhQxAdHY2UlBQIIRAaGornn38eL7zwAgDAYDAgJCQE69atw/jx4626v5ZcUNGehRHNNbX2Lbi/3iKCz6w/gh3HOlbriH8nD1kSeHN3V27RJUz45ECT5/l8xhCuLURERA2y5flt92DpqqoqpKenY9q0aVAoah/SQ4cOxcaNG1FWVgaTyYQNGzagoqIC9913HwAgNzcX/v7+UhIEAPHx8VAqlcjLy5Ni7rnnHikJAoCEhAQUFhbi8uXLUkx8fLzsehISEpCbmwsAKC4uhk6nk8Wo1WrExsZKMZZUVlbCaDTKXi3FnoURG5pRtetYSYdLggBA5a7EP56Klbr29i24Xzbmx57lBYiIiJrD7kQoIyMDer0eU6dOlbZt2rQJ1dXVCAwMhJeXF2bNmoWtW7eiV69eAGrHEAUHB8vO4+7ujoCAAOh0OikmJCREFmN+31RM3f11j7MUY8ny5cuhVqulV1hYmFXfhTWa8/Cue2yNSeDFL461xCU5nM5YCaVCgYejb0Fcz8B6rVz2LC9ARETUHHYnQqmpqRg5ciRCQ0OlbYsXL4Zer0d2dja+++47zJ8/H2PHjsXx48db5GJb26JFi2AwGKTXzz//3GLnDvLxsvvYug/+93NO4mrljZa4pDbRWEJoz/ICREREzWHX9PkzZ84gOzsbW7ZskbYVFRXh/fffR0FBAfr16wcAGDBgAL755ht88MEHSElJgUajwYULF2TnunHjBsrKyqDRaAAAGo0GpaWlshjz+6Zi6u43b9NqtbKY6OjoBu/Ly8sLXl72JyyNsmNFRPMYIfODv8YkkLb/dIteVktQKOoP9m5IY605dZcXMC8nIH3Gf/+veWA1ERFRS7CrRSgtLQ3BwcFISkqStl27dq32hEr5Kd3c3GAymQAAcXFx0Ov1OHz4sLQ/JycHJpMJsbGxUszevXtRXf3boNqsrCz06dMHXbp0kWJ2794t+5ysrCzExcUBAMLDw6HRaGQxRqMReXl5UoyjXbxaaVO8pQf/weIy6K/Xn3HW1qxJgqxtzTGvmaRRyxMmjVpVr8grERFRc9ncImQymZCWloYpU6bA3f23wyMiItCrVy/MmjULb775JgIDA5GRkSFNkweAvn37IjExETNmzEBKSgqqq6vxzDPPYPz48VIX2+9//3ssW7YM06dPx4IFC1BQUIB33nkHb731lvRZc+fOxb333ovVq1cjKSkJGzZswHfffSdNsVcoFJg3bx5ef/119O7dG+Hh4Vi8eDFCQ0ORnJzcnO/LbraOa9FYWECwPQ8Snn53D3yRf87i0gC2tuYkRmnxYGTtwpIXrlQguLNKtsAkERFRS7E5EcrOzsbZs2cxbdo02XYPDw/s2rULCxcuxKhRo3D16lX06tULn332GR566CEp7h//+AeeeeYZPPDAA1AqlXjsscfw7rvvSvvVajX+9a9/Yc6cORg0aBCCgoKwZMkS2VpDQ4cOxfr16/HKK6/gpZdeQu/evZGRkYGoqCgp5sUXX0R5eTlmzpwJvV6PYcOGITMzEypV2wy0NY9/sWbmWICPJ14e2Rdqb098efSclAi050HC9/cNwUtJkXg/5yTS9p+WtVyF+HlhwuBuqLxhQm7RJauSGvOaSURERK2pWesIObuWXEcIqJ32/of19ctrWEPj54Wxd4YhdX8xyivbXzmNf0yPxd29gwDUjmU6WFwGneE69p+6iKwfL8BwveH1g4iIiFqSLc9v1hpzoC4+nk0HNUBnrMS7Oada8Gpa1oUrFcgtuiR1ZV0ur8SyHScsdpWZV83mmB8iImprTIQcKOtEw2sYdXSLvyzAVStbqgRqxw0t234CD0ZqOPaHiIjajN3rCJFtakwCGUfPt/VltBprkyCzhlbNJiIiciQmQg5ysLgMZeVVbX0Z7U57nglHRETOj4mQg/CBb1l7nglHRETOj4mQgwT5ttKK1R0Yy2UQEVFbYyLkKE60SEFLDG1WgOUyiIio7TERcpCL5daV2PDxUKKTZ/v9WZKjQ/G3aYObdY4unTykqfM1JoHcokv48ug55BZdQo3JiTJGIiJq9zh93kGsHQsz78E++J9dP7by1dhv3F3dbFoluy5/bw88eXcPPHN/b7gpFcgsKMGy7Sdk5+Fii0RE5EhMhBzEnDzoDBUWe8nMleaDOrffsUTeHm6IDvOXqsQ/nW7dKtnP/K4n7u7VVVZaI7OgBLPTj9T7Lm5ebNG8SjVrjhERUWtgIuQg5uRhdvoRKCAfMlS3KKna2/7Vp1vb9eoaRC7NxMzh4Vj0UCQ+/H0Mnvn8CBrqzTInd8892EeWvNSYBJZtP2ExIay72KLJBLy2ky1GRETUetrvYBQnlBilxZqJMdCo5d1kAT6eePLuHlB7e2JQ9y7QqtvvlHIhgI/2FmPOPw7jRIkRD0aGWIxrrOL8weKyRrvVzIst/mH9kXpx5hajzIKS5twGERERABZdbVRLF101M3f3ZJ/QYevRcygr/60eV4CPJ0LVXig4f6XFPs8Rbm7laqzl5suj5zB3w9FmfZZGrcK+Bfezm4yIiOph0dV2zk2pgOF6FdbuP12ve6isvKpDrkBtvo/7bg/C8N5dMSmuBzzdaxscbx7n09w1leqW54jrGdi8CyciIpfGRKgNVN0w4aWtBc60tJBkz/9dxJ7/u4hP9xVj6ahIAKg3M0zj5wX/Th4WK9Pbgqt1ExFRczERcrDMghK8tPW4rDvMGekMFQ3OKis1VrZIEtjS5Tk4Q42IyPUwEXKghqaMO6PG7tE8M8zb0w3XqmyrWg/8NkaoJctzcE0jIiLXxFljDtLYlHFXJAC7kyCg8fIctq5WbU5QOUONiMj1sEXIQZqaMu6q/L09YLhe3WCCqFRAtk6RpolWGltbdqxd0+jBSA27yYiInBATIQfhwF7Lnrw7HG9n/1+Di0y+P2Eguvh4WTVux9rVquuydk0jzlAjInJOTIQcJMin/ZbOaCtKBdA72BdrJsbUn1lm4/gce1t2rE1QmcgSETknJkKOwl6VekwCmLO+tqVm34L7mzVjy96WHWtnnrX0DDUiImofmAg5yIUrlW19Ce2SwG8tNc3perK3ZWdweECjaxq1xgw1IiJqPzhrzEEuMhFqkLmlpjnsbdnJOqFrdGFHgcZnqBERUcfGRMhBLl9jItSYncfPWzXVvSGDwwOgVasa7IFUoHb2WN2WHfO4osb4d/LAg5Eau66JiIjaPyZCDqJU8KtuTPqBs5jwyQHc9T/Z2HXM+nV7zGsG7Th2HuPv6gag/nCshtYesmZJA/216ma3VhERUfvFMUIOclf3Lm19CR1CWXkV/rD+CGb9Eo5FD0U2GmtpzaBOnm5QKhS4WnlD2lZ3BlrdMhonS69YdU2cMUZE5LyYCDnIT1Y+dKnWR3uLMeBWfzx0R6hsuzmRyT6hQ+r+0/WOM69W7ePlhvF3hiE+UiPNQLOUOFmDM8aIiJwXEyEH+e40u1ds9cqXBUiI0krdWbYkMuWVNUjdfxp3/XdM0DvZ/4e3sk/afA1atQqDundBbtElFmMlInJCTIQc5FrVjaaDSKasvBoHii5BqVQ02ALUlIVbjuPVbSegM9rXvRV1ix/uXfU1i7ESETkpJkIOUlPDcqv2mP7ZIVTcMNl9fO3U+Ianxzcl68SFetsaK9lBREQdi01TmXr06AGFQlHvNWfOHJw+fdriPoVCgc2bNwMA1q1b12DMhQu1D5w9e/ZY3K/T6WTX8sEHH6BHjx5QqVSIjY3FwYMHZfsrKiowZ84cBAYGwtfXF4899hhKS0ub8101y8+Xr7XZZ3dkzUmCWos5pV22/YTd0/2JiKh9sCkROnToEEpKSqRXVlYWAGDMmDEICwuT7SspKcGyZcvg6+uLkSNHAgDGjRtXLyYhIQH33nsvgoODZZ9VWFgoi6u7f+PGjZg/fz6WLl2KI0eOYMCAAUhISJCSKQB47rnnsH37dmzevBn//ve/cf78eTz66KN2f1HNdcPU/h7oZL+6JTuIiKjjsqlrrGvXrrL3K1asQM+ePXHvvfdCoVBAo5EvPLd161aMHTsWvr6+AABvb294e3tL+3/99Vfk5OQgNTW13mcFBwfD39/f4nX89a9/xYwZM/Dkk08CAFJSUrBz506sXbsWCxcuhMFgQGpqKtavX4/7778fAJCWloa+ffviwIEDGDJkiC233SKCfFUovWJ/Fw21T5xaT0TUsdm9yl9VVRXS09Mxbdo0KBT1Z9AcPnwYR48exfTp0xs8x9/+9jd06tQJjz/+eL190dHR0Gq1ePDBB7F//37Z5x4+fBjx8fG/3YRSifj4eOTm5kqfXV1dLYuJiIhAt27dpBhLKisrYTQaZa+WEtalU4udi9oPTq0nIurY7E6EMjIyoNfrMXXqVIv7U1NT0bdvXwwdOrTBc6SmpuL3v/+9rJVIq9UiJSUFX3zxBb744guEhYXhvvvuw5EjRwAAFy9eRE1NDUJCQmTnCgkJkcYR6XQ6eHp61mtRqhtjyfLly6FWq6VXWFhYY1+BTfJ/vtxi56L2QePnxWKsREQdnN2zxlJTUzFy5EiEhobW23f9+nWsX78eixcvbvD43Nxc/Pjjj/j73/8u296nTx/06dNHej906FAUFRXhrbfeqhfb0hYtWoT58+dL741GY4slQ+WVnD7vbCYM7sb1hIiIOji7EqEzZ84gOzsbW7Zssbj/f//3f3Ht2jVMnjy5wXN8+umniI6OxqBBg5r8vMGDB2Pfvn0AgKCgILi5udWbAVZaWiqNUdJoNKiqqoJer5e1CtWNscTLywteXl5NXo89TBws7XR6BPm02Lnqlv7goo1ERI5jV9dYWloagoODkZSUZHF/amoqRo8eXW9wtdnVq1exadOmRscP1XX06FFotbXrtXh6emLQoEHYvXu3tN9kMmH37t2Ii4sDAAwaNAgeHh6ymMLCQpw9e1aKcTR3NxZddTYtNT4os6AEw97IwYRPDmDuhqOY8MkBDHsjB5kF1hefJSIi+9jcImQymZCWloYpU6bA3b3+4adOncLevXuxa9euBs+xceNG3LhxAxMnTqy37+2330Z4eDj69euHiooKfPrpp8jJycG//vUvKWb+/PmYMmUK7rzzTgwePBhvv/02ysvLpVlkarUa06dPx/z58xEQEAA/Pz88++yziIuLa5MZYwDA9RSdi1atapHxQZkFJZidfgQ3/+fBRRuJiBzD5kQoOzsbZ8+exbRp0yzuX7t2LW699VaMGDGiwXOkpqbi0UcftTg9vqqqCs8//zzOnTuHTp064Y477kB2djZ+97vfSTHjxo3Dr7/+iiVLlkCn0yE6OhqZmZmyAdRvvfUWlEolHnvsMVRWViIhIQEffvihrbfbYrjunnO5Xl2DrBO6ZiUpNSaBZdtP1EuCgNp1ihSoXbTxwUgNu8ksYHciEbUEhRCCj+gGGI1GqNVqGAwG+Pn5NetcPRbubKGrovbA/LhtTotNbtElTPjkQJNxn88YgriegdL7xhIAZ00Obr6vy+WVeG3nj6wBR0QW2fL8Zq0xIju0RIuNtYsx1o3LLCjBsu0nLCYA+O/1OFtyYOmeLWF3IhHZgyN4HcStrS+AWpy5zMa6/cV21RyzdrC1Oc48nujmhEBnqMDT6UfwdAP7Zqcf6bADrxu6Z0tYA46I7MFEyFE6fu8ENeC1nT/aNctrcHgAtGpVg/9pKPDboOymxhM1pCMnB43dc0NYA46IbMVEyEE4a8y52dPy4qZUSF1aNydD5vdLR0XCTanAweIyq1pFLOmoyUFz7pk14IjIWkyEiFqAvS0viVFarJkYA41a3k2mUatkY11a4sFuzzlqTAK5RZfw5dFzyC265NBWpebcM2vAEZG1OFiaqIWYW17eyvo/3N0ryOoZW4lRWjwYqWl0tldLPNhtPUdjA7MdMRjZnntWoDaJZA04ovavvcxyZSJE1MLe//oU3v/6lE1Jg5tSIZsifzPzeCKdocKmMTNmXTp52JQctIeFHm2955u7E4mo/WrrP7TqYtcYUSsxJw27jpU0u3upsfFE1rDlE60ZmO2Iwde23vPN3YlE1D41NgO2LWa5skWIqJWY04RnPj8iW1nc3r96zOOJbv4rKsDHA2Xl1Y0eq79WjYPFZY22Opk1NUi57uBra87XHA3ds1atwuKkSHTx8WzzZnUisl57XFGfiRBRK7u54aQ53UuWxhPpjBV4buPRJo+1ZwHHlohrLmvGUBFRx9Ce/tAyYyJE5GDN/avn5vFEuUWXrDrO1gUcWyquJTQ1hoqIOob29ocWwDFCRG2i7gyz5k5Lt2VhxrY4HxGRWXv8Q4uJEFEbev/rU5jwyQG7VqY2s2VhxrY4HxGRWXv8Q4uJEFE70NzZEtYuzNhW5yMiAtrnH1oKIQSLPzTAaDRCrVbDYDDAz8+vWefqsXBnC10VdRRKRf2B0k0J9PFE7qIH4Olu398oLb1AWXtZ8IyInEtrryNky/ObiVAjmAiRPZ75XS/c3SsIOT/p8Mk3p20+PsDHA395pL/FfwwaSkyYsBBRR9Oa/27Z8vzmrDGiFmIu7/DHB3pjzZ5TdiVBAFBWXm1xer2lv6ACfDzxWMwt2HGspF2s0EpEZK32MhuUY4SIWoD5b5jRA7S4Z+XXeCv7ZLPPWXf15oZWYi0rr8In3xS3mxVaiYg6GiZCRFZSAJj7QG98+PuB0FoYRDzznnB8vLcYOmPz17+ou6hYYyuxNnY84JhSGEREHRm7xoisJAC8s/skUibGYN+C+2V924O6d8G9q762qyBqYy5cqWhyJdbGrtfRK7SSY3FsGFHzMREistHCLcfR2csDQ3oGSg+d3KJLdiUrTQnurGr2CquOXKGVHKc9Ve8m6sjYNUZkI/21ajyRmidbBLGlk426i4o1d4VVR67QSo7R3qp3E3VkTISI7FTy34fOrmPncfFKZYud9+ZFxQaHByDAx9Ou87AUhvNpqno3wLFhRLZgIkTUDALAM5/n47WdP9p9DrW3vIf65tWb3ZQKvP5wlE3nZCkM52VL9W4iahrHCBE1U3P+8J4xPBwLR/ZtcsDrQ3doMeuXcHy0t9iq82o4VsRptcfq3UQdGRMhojaS1F+Dl5Nqa+5YM6tr0UORGHCrP175sgBl5dXSdq1ahcVJkeji48nZQy6gPVbvJurImAgRtZER/TQ2H/PQHaFIiNJyyrQLM1fv1hkqLI4TMq9wzrFhRNZhIkTURuz9i729LEtPbcNcvXt2+hEoAFkyxLFhRLbjYGkH6dHF9lk/5Jw4m4uaKzFKizUTY6CxsML5zTXqiKhxbBFykC1z7kXM61ltfRnUxvgXO7WUxCgtHozUsJuUqJlsahHq0aMHFApFvdecOXNw+vRpi/sUCgU2b94sncPS/g0bNsg+Z8+ePYiJiYGXlxd69eqFdevW1buWDz74AD169IBKpUJsbCwOHjwo219RUYE5c+YgMDAQvr6+eOyxx1BaWmrL7baoAF9PdPVlq1BztMY/7106ecC/k4dsm4+nG+Y+0Asf/j6mXk0xswAfD8wYHg6Fhesyb5t1T7jFmmT8i51airmb9OHoWxBXZ6VzIrKeQghh9eTfX3/9FTU1NdL7goICPPjgg/j6668xfPhw/Prrr7L4jz/+GKtWrUJJSQl8fX1rP1ChQFpaGhITE6U4f39/qFS1D4zi4mJERUXh6aefxlNPPYXdu3dj3rx52LlzJxISEgAAGzduxOTJk5GSkoLY2Fi8/fbb2Lx5MwoLCxEcHAwAmD17Nnbu3Il169ZBrVbjmWeegVKpxP79+63+coxGI9RqNQwGA/z8/Kw+rjF3vZ6FX69Wtci5nIVSIZ+CrlAAdf+r9Pf2wJN398Ds+3phzZ5TSNt/Gvrr1fVP1ISGZlcBaPCvanMtJ52xAmVXKxHg4wmN2luKaarMAWtBERE5ni3Pb5sSoZvNmzcPO3bswMmTJ6FQ1P/HfeDAgYiJiUFqaupvH6hQYOvWrUhOTrZ4zgULFmDnzp0oKCiQto0fPx56vR6ZmZkAgNjYWNx11114//33AQAmkwlhYWF49tlnsXDhQhgMBnTt2hXr16/H448/DgD46aef0LdvX+Tm5mLIkCFW3V9rJEIAUHa1CmNS9uFs2XWYBKD2dkO3AB/4d/LEL2XXUF5Vg+6BnXBLF2/knrqEX69Wwl1Z25pQdrUSxsran8xDCZhMwI0WuzLLFAB6BqlQUW1C2bUbEEJAqQAqqgXc3IBOnm4YcKs/Qv07obzyBs7pr8PLXYGgzt64xd8b/p08YLhWjfOG6wj1VyHutiAoFQpcLK+UCpYePnNZVsC07vubkwdzclGiv478ny9DAAgP9MHvY7vjyNnLyC26BEAgtkcglG4KXLxa2apJCJMdIqL2xabnt7BTZWWlCAwMFP/zP/9jcf93330nAIj9+/fLtgMQoaGhIjAwUNx1110iNTVVmEwmaf/w4cPF3LlzZcesXbtW+Pn5SZ/r5uYmtm7dKouZPHmyGD16tBBCiN27dwsA4vLly7KYbt26ib/+9a8N3lNFRYUwGAzS6+effxYAhMFgaOyrICIionbEYDBY/fy2e7B0RkYG9Ho9pk6danF/amoq+vbti6FDh8q2//nPf8b999+PTp064V//+hf+8Ic/4OrVq/jjH/8IANDpdAgJCZEdExISAqPRiOvXr+Py5cuoqamxGPPTTz9J5/D09IS/v3+9GJ1O1+A9LV++HMuWLbPm9omIiMgJ2J0IpaamYuTIkQgNDa237/r161i/fj0WL15cb1/dbQMHDkR5eTlWrVolJUJtadGiRZg/f7703mg0IiwsrA2viIiIiFqTXesInTlzBtnZ2Xjqqacs7v/f//1fXLt2DZMnT27yXLGxsfjll19QWVlbvVuj0dSb3VVaWgo/Pz94e3sjKCgIbm5uFmM0Go10jqqqKuj1+gZjLPHy8oKfn5/sRURERM7LrkQoLS0NwcHBSEpKsrg/NTUVo0ePRteuXZs819GjR9GlSxd4eXkBAOLi4rB7925ZTFZWFuLi4gAAnp6eGDRokCzGZDJh9+7dUsygQYPg4eEhiyksLMTZs2elGCIiIiKbu8ZMJhPS0tIwZcoUuLvXP/zUqVPYu3cvdu3aVW/f9u3bUVpaiiFDhkClUiErKwt/+ctf8MILL0gxTz/9NN5//328+OKLmDZtGnJycrBp0ybs3LlTipk/fz6mTJmCO++8E4MHD8bbb7+N8vJyPPnkkwAAtVqN6dOnY/78+QgICICfnx+effZZxMXFWT1jjIiIiFyArSOxv/rqKwFAFBYWWty/aNEiERYWJmpqaurt++c//ymio6OFr6+v8PHxEQMGDBApKSn1Yr/++msRHR0tPD09xW233SbS0tLqneu9994T3bp1E56enmLw4MHiwIEDsv3Xr18Xf/jDH0SXLl1Ep06dxCOPPCJKSkpsuldbRp0TERFR+2DL87tZ6wg5u9ZaR4iIiIhajy3PbxZdJSIiIpfFRIiIiIhcFqvPN8Lca2g0Gtv4SoiIiMha5ue2NaN/mAg14sqVKwDARRWJiIg6oCtXrkCtVjcaw8HSjTCZTDh//jw6d+5ssahsc5hXrf755585ELsd4O/RvvD3aF/4e7Qv/D2aJoTAlStXEBoaCqWy8VFAbBFqhFKpxK233tqqn8EVrNsX/h7tC3+P9oW/R/vC36NxTbUEmXGwNBEREbksJkJERETkspgItREvLy8sXbpUqrFGbYu/R/vC36N94e/RvvD3aFkcLE1EREQuiy1CRERE5LKYCBEREZHLYiJERERELouJEBEREbksJkJt4IMPPkCPHj2gUqkQGxuLgwcPtvUldXivvvoqFAqF7BURESHtr6iowJw5cxAYGAhfX1889thjKC0tlZ3j7NmzSEpKQqdOnRAcHIw//elPuHHjhixmz549iImJgZeXF3r16oV169Y54vbavb1792LUqFEIDQ2FQqFARkaGbL8QAkuWLIFWq4W3tzfi4+Nx8uRJWUxZWRmeeOIJ+Pn5wd/fH9OnT8fVq1dlMceOHcPw4cOhUqkQFhaGlStX1ruWzZs3IyIiAiqVCv3798euXbta/H7bu6Z+j6lTp9b730tiYqIshr9Hy1m+fDnuuusudO7cGcHBwUhOTkZhYaEsxpH/RvEZdBNBDrVhwwbh6ekp1q5dK3744QcxY8YM4e/vL0pLS9v60jq0pUuXin79+omSkhLp9euvv0r7n376aREWFiZ2794tvvvuOzFkyBAxdOhQaf+NGzdEVFSUiI+PF/n5+WLXrl0iKChILFq0SIr5z3/+Izp16iTmz58vTpw4Id577z3h5uYmMjMzHXqv7dGuXbvEyy+/LLZs2SIAiK1bt8r2r1ixQqjVapGRkSG+//57MXr0aBEeHi6uX78uxSQmJooBAwaIAwcOiG+++Ub06tVLTJgwQdpvMBhESEiIeOKJJ0RBQYH4/PPPhbe3t/joo4+kmP379ws3NzexcuVKceLECfHKK68IDw8Pcfz48Vb/DtqTpn6PKVOmiMTERNn/XsrKymQx/D1aTkJCgkhLSxMFBQXi6NGj4qGHHhLdunUTV69elWIc9W8Un0H1MRFysMGDB4s5c+ZI72tqakRoaKhYvnx5G15Vx7d06VIxYMAAi/v0er3w8PAQmzdvlrb9+OOPAoDIzc0VQtQ+OJRKpdDpdFLMmjVrhJ+fn6isrBRCCPHiiy+Kfv36yc49btw4kZCQ0MJ307Hd/OA1mUxCo9GIVatWSdv0er3w8vISn3/+uRBCiBMnTggA4tChQ1LMP//5T6FQKMS5c+eEEEJ8+OGHokuXLtLvIYQQCxYsEH369JHejx07ViQlJcmuJzY2VsyaNatF77EjaSgRevjhhxs8hr9H67pw4YIAIP79738LIRz7bxSfQfWxa8yBqqqqcPjwYcTHx0vblEol4uPjkZub24ZX5hxOnjyJ0NBQ3HbbbXjiiSdw9uxZAMDhw4dRXV0t+94jIiLQrVs36XvPzc1F//79ERISIsUkJCTAaDTihx9+kGLqnsMcw9+uccXFxdDpdLLvTq1WIzY2Vvb9+/v7484775Ri4uPjoVQqkZeXJ8Xcc8898PT0lGISEhJQWFiIy5cvSzH8jayzZ88eBAcHo0+fPpg9ezYuXbok7ePv0boMBgMAICAgAIDj/o3iM8gyJkIOdPHiRdTU1Mj+QwaAkJAQ6HS6Nroq5xAbG4t169YhMzMTa9asQXFxMYYPH44rV65Ap9PB09MT/v7+smPqfu86nc7i72Le11iM0WjE9evXW+nOOj7z99fYf/c6nQ7BwcGy/e7u7ggICGiR34j/+5JLTEzE3/72N+zevRtvvPEG/v3vf2PkyJGoqakBwN+jNZlMJsybNw933303oqKiAMBh/0bxGWQZq8+TUxg5cqT0/99xxx2IjY1F9+7dsWnTJnh7e7fhlRG1P+PHj5f+//79++OOO+5Az549sWfPHjzwwANteGXOb86cOSgoKMC+ffva+lLov9gi5EBBQUFwc3OrNxOgtLQUGo2mja7KOfn7++P222/HqVOnoNFoUFVVBb1eL4up+71rNBqLv4t5X2Mxfn5+TLYaYf7+GvvvXqPR4MKFC7L9N27cQFlZWYv8RvzfV+Nuu+02BAUF4dSpUwD4e7SWZ555Bjt27MDXX3+NW2+9VdruqH+j+AyyjImQA3l6emLQoEHYvXu3tM1kMmH37t2Ii4trwytzPlevXkVRURG0Wi0GDRoEDw8P2fdeWFiIs2fPSt97XFwcjh8/LvvHPysrC35+foiMjJRi6p7DHMPfrnHh4eHQaDSy785oNCIvL0/2/ev1ehw+fFiKycnJgclkQmxsrBSzd+9eVFdXSzFZWVno06cPunTpIsXwN7LdL7/8gkuXLkGr1QLg79HShBB45plnsHXrVuTk5CA8PFy231H/RvEZ1IC2Hq3tajZs2CC8vLzEunXrxIkTJ8TMmTOFv7+/bCYA2e75558Xe/bsEcXFxWL//v0iPj5eBAUFiQsXLgghaqemduvWTeTk5IjvvvtOxMXFibi4OOl489TUESNGiKNHj4rMzEzRtWtXi1NT//SnP4kff/xRfPDBB5w+/19XrlwR+fn5Ij8/XwAQf/3rX0V+fr44c+aMEKJ2+ry/v7/48ssvxbFjx8TDDz9scfr8wIEDRV5enti3b5/o3bu3bLq2Xq8XISEhYtKkSaKgoEBs2LBBdOrUqd50bXd3d/Hmm2+KH3/8USxdutQlp2s39ntcuXJFvPDCCyI3N1cUFxeL7OxsERMTI3r37i0qKiqkc/D3aDmzZ88WarVa7NmzR7ZkwbVr16QYR/0bxWdQfUyE2sB7770nunXrJjw9PcXgwYPFgQMH2vqSOrxx48YJrVYrPD09xS233CLGjRsnTp06Je2/fv26+MMf/iC6dOkiOnXqJB555BFRUlIiO8fp06fFyJEjhbe3twgKChLPP/+8qK6ulsV8/fXXIjo6Wnh6eorbbrtNpKWlOeL22r2vv/5aAKj3mjJlihCidgr94sWLRUhIiPDy8hIPPPCAKCwslJ3j0qVLYsKECcLX11f4+fmJJ598Uly5ckUW8/3334thw4YJLy8vccstt4gVK1bUu5ZNmzaJ22+/XXh6eop+/fqJnTt3ttp9t1eN/R7Xrl0TI0aMEF27dhUeHh6ie/fuYsaMGfUehPw9Wo6l3wKA7N8PR/4bxWeQnEIIIRzdCkVERETUHnCMEBEREbksJkJERETkspgIERERkctiIkREREQui4kQERERuSwmQkREROSymAgRERGRy2IiRERERC6LiRARERG5LCZCRERE5LKYCBEREZHLYiJERERELuv/AV+L+fpmDpDsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [0. for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    hdCPpop[t] = tractPop[t]\n",
    "#hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]  #may not work for states w/cut districts\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7137f13d-05fe-4e85-a53a-43132e83fe75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYlower\n"
     ]
    }
   ],
   "source": [
    "print(STATE)\n",
    "date = \"01May\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_\"+date+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7194\n"
     ]
    }
   ],
   "source": [
    "print(nUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f4883ddf-96f0-46a9-9d58-5c9664e90b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7775170.0 20201249.0 2992551.0313198953 20201249\n"
     ]
    }
   ],
   "source": [
    "print(statePop,trueStatePop,np.sum(hdCPpop),np.sum(tractPop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/NYlower14191convexHD2_15nW4_01May.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.5766934509840631 but is now 1.0000000000000002\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "#if this is a cold restart, would need to add block to read in unit topology before below block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 6001 last HD had pop 776297.0\n",
      "working on vtd --> unit conversion for HD 8001 last HD had pop 778163.0\n",
      "working on vtd --> unit conversion for HD 11001 last HD had pop 775654.0\n",
      "working on vtd --> unit conversion for HD 12001 last HD had pop 776100.0\n",
      "working on vtd --> unit conversion for HD 13001 last HD had pop 564741\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 495747.0 would be used 1.8 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 52630.0 would be used 2.143 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 495747.0 would be used 1.8002879502774103 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 52630.0 would be used 2.1431540177759683 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n",
      "final unit use of CCB 0 is 1.00076 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 1.00005 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < 1.00  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 1.00358 0.07818\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: underused < 0.5; below overused > 1.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 7263 populated HDs out of 14191\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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jxgw9+eSTevPNN7V3717dc889crvd9jewhgwZottvv13Tp0/X9u3btXXrVmVmZmry5Mlyu92SpClTpig8PFxpaWmqrq7WmjVrtHjxYmVlZdl9PPjggyouLtaiRYt04MABLViwQDt37lRmZuZ3XxUAABD0wtp7wM6dO3Xrrbfar1uDx7Rp01RQUKDZs2eroaFB6enpqq+v180336zi4mJFRkbax6xcuVKZmZm67bbbFBoaqokTJ2rJkiX2uNPp1KZNm5SRkaGRI0eqb9++ysnJ8XuWzo033qhVq1bpkUce0e9+9ztdffXVKiws1LXXXntBCwEAAMzynZ6TE+x4To4/npMDAAgGXfKcHAAAgIsFIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASAEPOWfOnNGjjz6qgQMHKioqSldeeaWeeOIJWZZl11iWpZycHPXr109RUVFKTEzUhx9+6DfPsWPHlJqaKofDoejoaKWlpenkyZN+NXv27NGYMWMUGRmpuLg45eXlBfpyAABAkAp4yHnmmWe0bNkyvfzyy9q/f7+eeeYZ5eXl6aWXXrJr8vLytGTJEuXn56uiokI9evRQUlKSTp06ZdekpqaqurpaJSUlKioqUllZmdLT0+1xn8+ncePGacCAAaqsrNSzzz6rBQsWaPny5YG+JAAAEIRCrK/eYgmAO+64Q7GxsXrttdfsfRMnTlRUVJT+9Kc/ybIsud1uPfTQQ3r44YclSV6vV7GxsSooKNDkyZO1f/9+DR06VDt27NCoUaMkScXFxRo/fryOHDkit9utZcuWad68efJ4PAoPD5ckZWdnq7CwUAcOHDivXn0+n5xOp7xerxwORyCXQZdnrw/ofJ3hk4XJXd0CAADndL7v3wG/k3PjjTeqtLRUH3zwgSTpv//7v/Xuu+/qpz/9qSTp4MGD8ng8SkxMtI9xOp2Kj49XeXm5JKm8vFzR0dF2wJGkxMREhYaGqqKiwq4ZO3asHXAkKSkpSTU1NTp+/HibvTU2Nsrn8/ltAADATGGBnjA7O1s+n0+DBw9Wt27ddObMGT311FNKTU2VJHk8HklSbGys33GxsbH2mMfjUUxMjH+jYWHq3bu3X83AgQPPmqN1rFevXmf1lpubq8ceeywAVwkAAC52Ab+T8/rrr2vlypVatWqVdu3apRUrVui5557TihUrAn2qdps7d668Xq+9HT58uKtbAgAAHSTgd3JmzZql7OxsTZ48WZI0bNgwffrpp8rNzdW0adPkcrkkSbW1terXr599XG1trUaMGCFJcrlcqqur85u3ublZx44ds493uVyqra31q2l93VrzdREREYqIiPjuFwkAAC56Ab+T88UXXyg01H/abt26qaWlRZI0cOBAuVwulZaW2uM+n08VFRVKSEiQJCUkJKi+vl6VlZV2zebNm9XS0qL4+Hi7pqysTE1NTXZNSUmJBg0a1OavqgAAwPdLwEPOhAkT9NRTT2n9+vX65JNPtG7dOj3//PP613/9V0lSSEiIZsyYoSeffFJvvvmm9u7dq3vuuUdut1spKSmSpCFDhuj222/X9OnTtX37dm3dulWZmZmaPHmy3G63JGnKlCkKDw9XWlqaqqurtWbNGi1evFhZWVmBviQAABCEAv7rqpdeekmPPvqo7r//ftXV1cntduvXv/61cnJy7JrZs2eroaFB6enpqq+v180336zi4mJFRkbaNStXrlRmZqZuu+02hYaGauLEiVqyZIk97nQ6tWnTJmVkZGjkyJHq27evcnJy/J6lAwAAvr8C/pycYMJzcvzxnBwAQDDosufkAAAAXAwIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACM1CEh5+9//7t+8YtfqE+fPoqKitKwYcO0c+dOe9yyLOXk5Khfv36KiopSYmKiPvzwQ785jh07ptTUVDkcDkVHRystLU0nT570q9mzZ4/GjBmjyMhIxcXFKS8vryMuBwAABKGAh5zjx4/rpptuUvfu3fWXv/xF77//vhYtWqRevXrZNXl5eVqyZIny8/NVUVGhHj16KCkpSadOnbJrUlNTVV1drZKSEhUVFamsrEzp6en2uM/n07hx4zRgwABVVlbq2Wef1YIFC7R8+fJAXxIAAAhCIZZlWYGcMDs7W1u3btXf/va3Nscty5Lb7dZDDz2khx9+WJLk9XoVGxurgoICTZ48Wfv379fQoUO1Y8cOjRo1SpJUXFys8ePH68iRI3K73Vq2bJnmzZsnj8ej8PBw+9yFhYU6cODAefXq8/nkdDrl9XrlcDgCcPX/7/Ls9QGdrzN8sjC5q1sAAOCczvf9O+B3ct58802NGjVK//Zv/6aYmBhdd911euWVV+zxgwcPyuPxKDEx0d7ndDoVHx+v8vJySVJ5ebmio6PtgCNJiYmJCg0NVUVFhV0zduxYO+BIUlJSkmpqanT8+PE2e2tsbJTP5/PbAACAmQIecj7++GMtW7ZMV199tTZu3Kjf/OY3+u1vf6sVK1ZIkjwejyQpNjbW77jY2Fh7zOPxKCYmxm88LCxMvXv39qtpa46vnuPrcnNz5XQ67S0uLu47Xi0AALhYBTzktLS06Prrr9fTTz+t6667Tunp6Zo+fbry8/MDfap2mzt3rrxer70dPny4q1sCAAAdJOAhp1+/fho6dKjfviFDhujQoUOSJJfLJUmqra31q6mtrbXHXC6X6urq/Mabm5t17Ngxv5q25vjqOb4uIiJCDofDbwMAAGYKeMi56aabVFNT47fvgw8+0IABAyRJAwcOlMvlUmlpqT3u8/lUUVGhhIQESVJCQoLq6+tVWVlp12zevFktLS2Kj4+3a8rKytTU1GTXlJSUaNCgQX7f5AIAAN9PAQ85M2fO1Hvvvaenn35aH330kVatWqXly5crIyNDkhQSEqIZM2boySef1Jtvvqm9e/fqnnvukdvtVkpKiqR/3Pm5/fbbNX36dG3fvl1bt25VZmamJk+eLLfbLUmaMmWKwsPDlZaWpurqaq1Zs0aLFy9WVlZWoC8JAAAEobBAT/ijH/1I69at09y5c/X4449r4MCBevHFF5WammrXzJ49Ww0NDUpPT1d9fb1uvvlmFRcXKzIy0q5ZuXKlMjMzddtttyk0NFQTJ07UkiVL7HGn06lNmzYpIyNDI0eOVN++fZWTk+P3LB0AAPD9FfDn5AQTnpPjj+fkAACCQZc9JwcAAOBiQMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMFKHh5yFCxcqJCREM2bMsPedOnVKGRkZ6tOnjy699FJNnDhRtbW1fscdOnRIycnJuuSSSxQTE6NZs2apubnZr2bLli26/vrrFRERoauuukoFBQUdfTkAACBIdGjI2bFjh/7jP/5DP/zhD/32z5w5U2+99ZbWrl2rd955R0ePHtXPf/5ze/zMmTNKTk7W6dOntW3bNq1YsUIFBQXKycmxaw4ePKjk5GTdeuutqqqq0owZM3Tvvfdq48aNHXlJAAAgSHRYyDl58qRSU1P1yiuvqFevXvZ+r9er1157Tc8//7x+8pOfaOTIkfrjH/+obdu26b333pMkbdq0Se+//77+9Kc/acSIEfrpT3+qJ554QkuXLtXp06clSfn5+Ro4cKAWLVqkIUOGKDMzU3feeadeeOGFjrokAAAQRDos5GRkZCg5OVmJiYl++ysrK9XU1OS3f/Dgwerfv7/Ky8slSeXl5Ro2bJhiY2PtmqSkJPl8PlVXV9s1X587KSnJnqMtjY2N8vl8fhsAADBTWEdMunr1au3atUs7duw4a8zj8Sg8PFzR0dF++2NjY+XxeOyarwac1vHWsW+r8fl8+vLLLxUVFXXWuXNzc/XYY49d8HUBAIDgEfA7OYcPH9aDDz6olStXKjIyMtDTfydz586V1+u1t8OHD3d1SwAAoIMEPORUVlaqrq5O119/vcLCwhQWFqZ33nlHS5YsUVhYmGJjY3X69GnV19f7HVdbWyuXyyVJcrlcZ33bqvX1uWocDkebd3EkKSIiQg6Hw28DAABmCnjIue2227R3715VVVXZ26hRo5Sammr/d/fu3VVaWmofU1NTo0OHDikhIUGSlJCQoL1796qurs6uKSkpkcPh0NChQ+2ar87RWtM6BwAA+H4L+GdyevbsqWuvvdZvX48ePdSnTx97f1pamrKystS7d285HA498MADSkhI0OjRoyVJ48aN09ChQzV16lTl5eXJ4/HokUceUUZGhiIiIiRJ9913n15++WXNnj1bv/rVr7R582a9/vrrWr9+faAvCQAABKEO+eDxubzwwgsKDQ3VxIkT1djYqKSkJP3+97+3x7t166aioiL95je/UUJCgnr06KFp06bp8ccft2sGDhyo9evXa+bMmVq8eLEuu+wyvfrqq0pKSuqKSwIAABeZEMuyrK5uoqv4fD45nU55vd6Afz7n8uzgu6P0ycLkrm4BAIBzOt/3b/7tKgAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjBTzk5Obm6kc/+pF69uypmJgYpaSkqKamxq/m1KlTysjIUJ8+fXTppZdq4sSJqq2t9as5dOiQkpOTdckllygmJkazZs1Sc3OzX82WLVt0/fXXKyIiQldddZUKCgoCfTkAACBIBTzkvPPOO8rIyNB7772nkpISNTU1ady4cWpoaLBrZs6cqbfeektr167VO++8o6NHj+rnP/+5PX7mzBklJyfr9OnT2rZtm1asWKGCggLl5OTYNQcPHlRycrJuvfVWVVVVacaMGbr33nu1cePGQF8SAAAIQiGWZVkdeYLPPvtMMTExeueddzR27Fh5vV790z/9k1atWqU777xTknTgwAENGTJE5eXlGj16tP7yl7/ojjvu0NGjRxUbGytJys/P15w5c/TZZ58pPDxcc+bM0fr167Vv3z77XJMnT1Z9fb2Ki4vPqzefzyen0ymv1yuHwxHQ6748e31A5+sMnyxM7uoWAAA4p/N9/+7wz+R4vV5JUu/evSVJlZWVampqUmJiol0zePBg9e/fX+Xl5ZKk8vJyDRs2zA44kpSUlCSfz6fq6mq75qtztNa0zgEAAL7fwjpy8paWFs2YMUM33XSTrr32WkmSx+NReHi4oqOj/WpjY2Pl8Xjsmq8GnNbx1rFvq/H5fPryyy8VFRV1Vj+NjY1qbGy0X/t8vu92gQAA4KLVoXdyMjIytG/fPq1evbojT3PecnNz5XQ67S0uLq6rWwIAAB2kw0JOZmamioqK9Pbbb+uyyy6z97tcLp0+fVr19fV+9bW1tXK5XHbN179t1fr6XDUOh6PNuziSNHfuXHm9Xns7fPjwd7pGAABw8Qp4yLEsS5mZmVq3bp02b96sgQMH+o2PHDlS3bt3V2lpqb2vpqZGhw4dUkJCgiQpISFBe/fuVV1dnV1TUlIih8OhoUOH2jVfnaO1pnWOtkRERMjhcPhtAADATAH/TE5GRoZWrVql//qv/1LPnj3tz9A4nU5FRUXJ6XQqLS1NWVlZ6t27txwOhx544AElJCRo9OjRkqRx48Zp6NChmjp1qvLy8uTxePTII48oIyNDERERkqT77rtPL7/8smbPnq1f/epX2rx5s15//XWtXx9832oCAACBF/A7OcuWLZPX69WPf/xj9evXz97WrFlj17zwwgu64447NHHiRI0dO1Yul0tvvPGGPd6tWzcVFRWpW7duSkhI0C9+8Qvdc889evzxx+2agQMHav369SopKdHw4cO1aNEivfrqq0pKSgr0JQEAgCDU4c/JuZjxnBx/PCcHABAMLprn5AAAAHQFQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkYI+5CxdulSXX365IiMjFR8fr+3bt3d1SwAA4CIQ1CFnzZo1ysrK0vz587Vr1y4NHz5cSUlJqqur6+rWAABAFwvqkPP8889r+vTp+uUvf6mhQ4cqPz9fl1xyif7whz90dWsAAKCLhXV1Axfq9OnTqqys1Ny5c+19oaGhSkxMVHl5eZvHNDY2qrGx0X7t9XolST6fL+D9tTR+EfA5O1pHrAMAAIHW+n5lWda31gVtyPn888915swZxcbG+u2PjY3VgQMH2jwmNzdXjz322Fn74+LiOqTHYON8sas7AADg/J04cUJOp/Mbx4M25FyIuXPnKisry37d0tKiY8eOqU+fPgoJCQnYeXw+n+Li4nT48GE5HI6AzQt/rHPnYa07B+vcOVjnztGR62xZlk6cOCG32/2tdUEbcvr27atu3bqptrbWb39tba1cLlebx0RERCgiIsJvX3R0dEe1KIfDwR+gTsA6dx7WunOwzp2Dde4cHbXO33YHp1XQfvA4PDxcI0eOVGlpqb2vpaVFpaWlSkhI6MLOAADAxSBo7+RIUlZWlqZNm6ZRo0bphhtu0IsvvqiGhgb98pe/7OrWAABAFwvqkDNp0iR99tlnysnJkcfj0YgRI1RcXHzWh5E7W0REhObPn3/Wr8YQWKxz52GtOwfr3DlY585xMaxziHWu718BAAAEoaD9TA4AAMC3IeQAAAAjEXIAAICRCDkAAMBIhJwLtHTpUl1++eWKjIxUfHy8tm/f/q31a9eu1eDBgxUZGalhw4Zpw4YNndRpcGvPOr/yyisaM2aMevXqpV69eikxMfGc/1/wD+39eW61evVqhYSEKCUlpWMbNEh717q+vl4ZGRnq16+fIiIi9IMf/IC/P85De9f5xRdf1KBBgxQVFaW4uDjNnDlTp06d6qRug1NZWZkmTJggt9utkJAQFRYWnvOYLVu26Prrr1dERISuuuoqFRQUdGyTFtpt9erVVnh4uPWHP/zBqq6utqZPn25FR0dbtbW1bdZv3brV6tatm5WXl2e9//771iOPPGJ1797d2rt3byd3Hlzau85Tpkyxli5dau3evdvav3+/9e///u+W0+m0jhw50smdB5f2rnOrgwcPWv/8z/9sjRkzxvqXf/mXzmk2yLV3rRsbG61Ro0ZZ48ePt959913r4MGD1pYtW6yqqqpO7jy4tHedV65caUVERFgrV660Dh48aG3cuNHq16+fNXPmzE7uPLhs2LDBmjdvnvXGG29Ykqx169Z9a/3HH39sXXLJJVZWVpb1/vvvWy+99JLVrVs3q7i4uMN6JORcgBtuuMHKyMiwX585c8Zyu91Wbm5um/V33XWXlZyc7LcvPj7e+vWvf92hfQa79q7z1zU3N1s9e/a0VqxY0VEtGuFC1rm5udm68cYbrVdffdWaNm0aIec8tXetly1bZl1xxRXW6dOnO6tFI7R3nTMyMqyf/OQnfvuysrKsm266qUP7NMn5hJzZs2db11xzjd++SZMmWUlJSR3WF7+uaqfTp0+rsrJSiYmJ9r7Q0FAlJiaqvLy8zWPKy8v96iUpKSnpG+txYev8dV988YWamprUu3fvjmoz6F3oOj/++OOKiYlRWlpaZ7RphAtZ6zfffFMJCQnKyMhQbGysrr32Wj399NM6c+ZMZ7UddC5knW+88UZVVlbav9L6+OOPtWHDBo0fP75Tev6+6Ir3wqB+4nFX+Pzzz3XmzJmznqocGxurAwcOtHmMx+Nps97j8XRYn8HuQtb56+bMmSO3233WHyr8vwtZ53fffVevvfaaqqqqOqFDc1zIWn/88cfavHmzUlNTtWHDBn300Ue6//771dTUpPnz53dG20HnQtZ5ypQp+vzzz3XzzTfLsiw1Nzfrvvvu0+9+97vOaPl745veC30+n7788ktFRUUF/JzcyYGRFi5cqNWrV2vdunWKjIzs6naMceLECU2dOlWvvPKK+vbt29XtGK+lpUUxMTFavny5Ro4cqUmTJmnevHnKz8/v6taMsmXLFj399NP6/e9/r127dumNN97Q+vXr9cQTT3R1a/iOuJPTTn379lW3bt1UW1vrt7+2tlYul6vNY1wuV7vqcWHr3Oq5557TwoUL9de//lU//OEPO7LNoNfedf6f//kfffLJJ5owYYK9r6WlRZIUFhammpoaXXnllR3bdJC6kJ/pfv36qXv37urWrZu9b8iQIfJ4PDp9+rTCw8M7tOdgdCHr/Oijj2rq1Km69957JUnDhg1TQ0OD0tPTNW/ePIWGcj8gEL7pvdDhcHTIXRyJOzntFh4erpEjR6q0tNTe19LSotLSUiUkJLR5TEJCgl+9JJWUlHxjPS5snSUpLy9PTzzxhIqLizVq1KjOaDWotXedBw8erL1796qqqsrefvazn+nWW29VVVWV4uLiOrP9oHIhP9M33XSTPvroIztIStIHH3ygfv36EXC+wYWs8xdffHFWkGkNlhb/vGPAdMl7YYd9pNlgq1evtiIiIqyCggLr/ffft9LT063o6GjL4/FYlmVZU6dOtbKzs+36rVu3WmFhYdZzzz1n7d+/35o/fz5fIT8P7V3nhQsXWuHh4daf//xn63//93/t7cSJE111CUGhvev8dXy76vy1d60PHTpk9ezZ08rMzLRqamqsoqIiKyYmxnryySe76hKCQnvXef78+VbPnj2t//zP/7Q+/vhja9OmTdaVV15p3XXXXV11CUHhxIkT1u7du63du3dbkqznn3/e2r17t/Xpp59almVZ2dnZ1tSpU+361q+Qz5o1y9q/f7+1dOlSvkJ+sXrppZes/v37W+Hh4dYNN9xgvffee/bYLbfcYk2bNs2v/vXXX7d+8IMfWOHh4dY111xjrV+/vpM7Dk7tWecBAwZYks7a5s+f3/mNB5n2/jx/FSGnfdq71tu2bbPi4+OtiIgI64orrrCeeuopq7m5uZO7Dj7tWeempiZrwYIF1pVXXmlFRkZacXFx1v33328dP3688xsPIm+//Xabf+e2ru20adOsW2655axjRowYYYWHh1tXXHGF9cc//rFDewyxLO7FAQAA8/CZHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACM9H9ozeA+yo3XlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 1444 time is now 0\n",
      "working on HD 3627 time is now 189\n",
      "working on HD 5821 time is now 390\n",
      "working on HD 6591 time is now 594\n",
      "working on HD 7417 time is now 823\n",
      "working on HD 8140 time is now 1119\n",
      "working on HD 10473 time is now 1365\n",
      "working on HD 11201 time is now 1576\n",
      "working on HD 11913 time is now 1743\n",
      "working on HD 12631 time is now 1925\n",
      "working on HD 13604 time is now 2131\n",
      "out of 7263 total HDs, there were 5740.0 contiguous and 1055.0 complement-contiguous HDs\n",
      "1207 HDs had both discontiguity problems, while enclave-only = 5001 and discontig only= 316\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 1523 5001\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 737828 district pop vs 776661 target\n",
      "we'll also trim any HD with total islands' pop <= 15533\n",
      "working on HD 1488\n",
      "working on HD 5661\n",
      "working on HD 6666\n",
      "working on HD 7685\n",
      "working on HD 10143\n",
      "working on HD 11032\n",
      "working on HD 11949\n",
      "working on HD 12905\n",
      "Out of 1523 discontig HDs 1523 had discontig HDs.\n",
      "Of these, 1094 won't be under 737828 after all minor discontigys were shed, while 429 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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B9UM+zc5v8Cav37EvZGjmSNFcQE1hREREpC5KcmD7/0Lhd7BqOhhmjVMMA2KOKOMIFEPEGBZVli14nmXBAexU0I6v6UMeycFr3t/0Y4P3UNR1YbRoLKCmAlYREZFjKciGFy+CqoPQIQVi7WD6qMIWXMTsSIEQYhhQaRnEGRaxhkmV5S9wdWPnVu+tYRcxK67wsn7HPob2Tmqwj7Cz6ECdzovGAmrqGRERETkatwveudEfRAAqCvG43eSaScRiHnURs6c9l7PLTCHOsKi0DDxWLN9bGdzomR4yLBNOQ/ZQ+EyLN9fvPuZ5aYmOqCygpp4RERGRcNwuKM0Db8XhIHKIw1OE04gP7qQbjmHAbY73+YNnMrfa36eU9tznncQOuoftDTlSQ/ZQrN+xj/zSY4ebawefEJUF1BRGREREjlSSA2+MheIfsDp25euhT9H78zvocCAHC3+xaoJx7Ie7YcCjjheZ7rmFvzO4TiHEwL+gWUP2UNS1l6VncocGe89IaJhGRESkuoJseO1XsPdb8HkwXLl0WfUbrts3mb1mQo0FzKoLFKgGWBaYGHxN72AQMYDJ558Y9vqGWOI93Oqqde1lidaGe+oZERERCSjJgZcu8u+0y+HC0wxbEcscf6ITFWEvq7Ac7Lc6kmErpsqyEWv4a0lyrSSmeW8N1obYDFh47ZmMOqMbg3p0rrESatpxroRa2+qqsy49jXRnPPkud9h1RhqjNyYSCiMiIiKB+pD9O8FXCfjX3YgzrOBqqp2NmkEkUDPSwfBQYcWTayYFA8k2K5WJ3pkhU3ZNCzp3cAANu8Q71L66ar7LzdQ3vuSWn53IC5/vaJYb7imMiIhI21aQDW9PgP27oGMK/PpVzLfGY7OqsCzCrqZadWgBs9hDy7gbBqTYXBSaTnLNJPaTwC3e6SFBJKB6/UaMzWiQ6bvHWl3VAD74bx6LrhvIvI8atjemISiMiIhI21SQDXs2wv/8ASoPrcHhyoH/uYctfW7i5O+fr7GAWUCx5WSm9yZm21+jh60Qy/I/9AvozB3e31FIl1qLVRujLqOuq6vmuQ5y98hT2VfuoUsHO2nOdsfVG9NQFEZERKTtyf4b/GUch/sNqnHlcKrr+bCXVVkQa0CqrYQH7a8w1XsbT9sXcgA7c703sKfdKeRib/K6jLrOlpn30bfBXwd26o12EAHNphERkbamIBv+cj2HKydqBhKDmuuHWIeCSOWhIZoMWxGL7E8zzTuNMd457HKexcxfDQlef+TPg8ary6hPb0u+y82U5RvJ2pzX4O2JlMKIiIi0HQXZ8M1fw7xQ+zKq5VY8lZYR3E+m+mqqxSSyi26U0Z7Zo/sy6oxuPDduIGnO0HCQ5oznuXEDG60uY/CJXUh3xh912vGRor1Tb3URh5HPP/+c0aNH061bNwzD4L333jvmNWvWrGHgwIE4HA769OnDsmXL6tFUERGR41CQDYvPhc//DOfdVefLKox2/NYznUrLFgwku6w0rvHMYpz3Xjo4u4QEjZH90ll7z0W8Oflsnho7gDcnn83aey5q1ALRGJvB7NF9gZq9MkcTzZ16q4s4jFRUVNC/f38WLVpUp/N37NjBpZdeyrBhw9i0aRN33HEHN998Mx9//HHEjRUREYmI2+XfZde1B0p2g3VoU7u1j8NZt9TpR6Syn2eT3mTDkKcxjViIa0fZZUu5aew1vDD5orBBIzBL5vIB3RnaO6lJ6jJG9ksP2ytTF9HYqbc6w7KOtsXPMS42DP76179yxRVX1HrOPffcw0cffcTmzZuDx8aOHUtJSQlZWVl1ep/S0lKcTicul4vExMT6NldERNqSkhx4cywU/eCfsntjFuT9F1ZcFzwlTPlq7ZyZMOoxSP0JdKp9g7to85lWcO2SojJPSNFqbd6cfHaD7hAcUNfnd6PXjKxbt47hw4eHHBsxYgTr1q2r9RqPx0NpaWnIl4iISJ2V5MAb10Chf0l3XDmwdCSk94fzfh887cggEvrP82qv2uKgQ1foMbRZBxEI7ZWZeO6JR60lMfDPqonWyqsBjR5G8vPzSU1NDTmWmppKaWkpBw8eDHvN/PnzcTqdwa/MzOb9Gy8iIs1IQTa8doW/R8TygRHjP+7KgefOgbWP1bjEZx1eTfVwIDnUbxLXHq5/B8a/B/HOpvkMDeRotSTNYeXVgGY5m2bmzJm4XK7gV05OTrSbJCIizV1JDmx4FV64EIq3glkFtlh/IAk8et2usJfagKc9l4UEEgsDrnwJpq6H3he2uCASUFstSWPP8IlEoy96lpaWRkFBQcixgoICEhMTadeuXdhrHA4HDoejsZsmIiKtRUkOLDwLqo7ocTer8EcNs8YllgX/V9Wbs2K3YRhwm+MDnvZcxm2OD7CAT05/jJFnXN0UrW90Db0PTkNr9DAydOhQVq1aFXLs008/ZejQoY391iIi0tq5XeAph/JCqPLUclLNIAJQZrXjdt8d9PXt5CXH48FA8qTnClYxlHNiz2Zk47W8yTXUPjiNIeJhmvLycjZt2sSmTZsA/9TdTZs2sXv3bsA/xDJ+/Pjg+b/97W/Zvn07d999N9999x3PPvssf/nLX7jzzjsb5hOIiEjbVJIDr4yGZaMgIRVu/oS6PNYCNSGJtoO8bZ9LNj252XMXlgUmBqsYylYy6dEl/N4y0vAintq7Zs0ahg0bVuP4hAkTWLZsGRMnTmTnzp2sWbMm5Jo777yT7OxsMjIymDVrFhMnTqzze2pqr4iIhCjJgdevgeIf/EMxnXvCxI+gLA9eGl7rZZUWxB1a0j3u0G68uWYyV3tncyo7ySWFrWRiM+C7eZdgj22WpZUtRl2f38e1zkhTURgREZGgkhx449dQ9P3hItVAIBk0Cf4+O+xlgeLUSstGnGFSaRmYxPCddQLjvPeG7LJ70aldWTpxcBN9oNarrs9v7dorIiItQ0kO7NsOn8yqGUSMGNi/s/YgAtX2ljGptGxstbpxj3cyO+geEkQAJp/fu/E/jwQpjIiISPNXkgPPDoEqN3RIqRlELN9RLzcIDSQ7rRRu9N5NHsk1zm0Oi4C1NRoMExGR5s3tgoJv/LNlTB9UFEJC+qEgYqs9iJz3e7j57wQedQb+IOImjineO8MGEYPmsQhYW6MwIiIizVNJDmz/X1h+FVbWPXx7/iJMIwZMH1ZZAdgTD298F8437/hDS7VZNmaMg6mxD7KVmit7pzejRcAais+0WLetmPc37WHdtmJ8ZvMsE1UBq4iIND/VhmXccUnEewrZZabwoPcGnnc8QaxRy6PLsPkDSvWi1omroCwfygsg7XR8iRms37GP/FI3+8o9dOlgJ83ZrlktAtYQsjbnMffDbPJch3fkTXfGM3t03yYLXCpgFRGRlqu8EKvKg2H6iHUXkWd1ooetkIfsS2rfZTcQQKr/N94Jjo7gHBQ8LQaa7eJfDSVrcx5Tlm/kyMiW73IzZfnGZtcDpGEaERFpPtwucO0hq6QbN9keotKyEWuYdDVK2W92IN1WQswRvSIWHK4hqR5Ekk6CMa+32D1l6stnWsz9MLtGEAGCx+Z+mN2shmwURkREJPrcLij8DpZfxYEXRvDg8k/5R/kJXO2ZQ9WhQNLZVhFyiWkZVFmGv6ekelGrLRaST4br34ZObW/X9/U79oUMzRzJAvJcbtbv2Nd0jToGhREREYmeaiGE16/CKsujfUUOb9rncQZbqSCeMsJvqlpgOfmNZ3qwqDUYSFJ+Atf9pU0GEYDCstqDSH3OawqqGRERkehwu/whpCwPMMCVi6d9N4rMZHrYClnpmIMJ2I3QGTM+y6DISiDdVsID9tfY8rNnOe3/TYVYB/x6OXQ9qc0NzVSXkhDfoOc1BYURERFpem4XFG2Fir3gygVnBjgziXflEIczODRTXYkZT0fDS6xhkkQ5eaa/qNX6ar6/NiT1J222N6S6wSd2Id0ZT77LHbZuxADSmtnCbhqmERGRplWSA8tGw7s3wlUv+affunIBC4+9M6k2V40gUmR25BLvo1zlmRNS1HowPgWjQ1foMVRB5JAYm8Hs0X0Basw8Cnzf3BZ2UxgREZGm43bBW9dD4Tf+vWTevTkkkDi8+8Nfhn9I4b/08Re1EgOx8bS7/k0Y926bHpYJZ2S/dJ4bN5A0Z+hQTFozXdhNi56JiEjjc7vAU+7/9bJR/iASmIbrzIT+18Hnf6pxWZGZgBsHGbYidpkpjPXOYuzwoUw7xUVMQqp6Q47BZ1qs37GPwjI3KQnxTb6wW12f3wojIiLSuAKFqhV7YeJH/mPLLj0USA7NhDlClWVjr+Uk3bafXDMJMMiwFXGgQybtb/kYnN2b9CNI/dT1+a1hGhERaVyecn8Q2b/TH0LAH0qcmTWCSKVlBItXfdjINZPJsBWT0C4Gy5lJ+85p/hVVpVVRGBERkcbl7O4PH517Hg4krj1QeaDGqYVWZ27x3MkuM4UMWzFgkWsms9vrxLzubdWHtFIKIyIi0vicGaGBZOkv4EBxyClVlo3utn3Mtr/Gbd5p7DJTKKQzE713c93Bu1lfnqIg0kopjIiISNNwZsCvXgg9ZsTwZf+57DJTiDVMKi0bPWyFPG1fyDTvNCZ4Z7CVDMpo36xWDJWGpTAiIiJNw5XrX1ukuo5dMXv/nLHeWewyU4g7FEhKac9OulFG++CpzWnFUGlYCiMiItL4XLmHakVyIcYBHVMhIQ3K8hn42ThSEh1ceyiQfGedwC3e6cEgYgDpzWzFUGlYWg5eREQal2vP4am8nXvCtSvAkQhYsOxSjP07WeaYyyXMYIx3FhW0C+kRgea3Yqg0LIURERGptzotquXoCB26+n898SN/7UjAxI848MJIdpQ5woYQgFt+dmKzWzFUGpbCiIiI1EvW5jzmfphNnutwYWm6M57Zo/uGhod4p39Krqe8xmJlvoTujK2cxQ6vLWwQAfjgv3ncPfI09Yy0YqoZERGRiGVtzmPK8o0hQQQg3+VmyvKNZG3OC70g3hl21dT1O/bxVWnHWoMIQJ7Lzfod+xqk3dI8KYyIiEhEfKbF3A+zw25PHzg298NsfOaxdxup63Td1jyt12darNtWzPub9rBuW3Gd7ltro2EaERE5usAmd4d6Ntbv2BfsEUmjuEath8Xh3oyhvZOO+qPrOl23tU3rDdTa/D07n79u2sO+isrga2GHulo5hREREQnP7YLSPPhg2uFN7pwZFJa5SaOYjhxkif0xiklkgndGjaGWuvRmDD6xC+nOePJd7rA9LQb+be9b07TecLU21QWGup4bN7DNBBIN04iISE2BnXZfvwrK8qvtKZNLhm0f79jn8JHjXnrYCkmilA4crPEj6tKbEWMzmD26L+APHtUFvm9N03prq7WpLtKhrtZAYURERGoK7LTrygUs/w67+3fC0pEM/PRqMmzFOIwqcs1kxnpnkc/h4ZhIFykb2S+d58YNJM0ZGl7SnPGtqnfgaLU2R6o+1NUWaJhGRERqCuy0G1iszJkBCengygn2WOwxk/m1dzZ5RwQRiLw3Y2S/dC7um3bsNUtasOq1NnXVmgt3q1MYERGR8AI77QYCyRFyLnoaa108VHvAph1H8WWMzThmwWtLVp9g0doKd2ujMCIi0pYdMVMmhGsPOBL8O+0u/UWNl8/+772s/e3fWL+vfavtzWhIkQSL1li4ezQKIyIibVWgSLViL77xR4SKLgeIefWX/sXKKvaGXpeQBrY42L+TmFd/ydCJH0HvjPDvIUHHmjl0pNZUuHssCiMiIm1VoEh1/07ynvo50933k0cS6RTzdvxDZFAARgxYPv/5Celgi/EXtTozDhe1LrsUJq4K37siQYGZQ1OWb8SAWgNJW1xnRLNpRETaKmd31gx9mV1mChkUsMI+j4HG96ywzyODAiotmz+IxDj8wePmv8ONH/t33q0+y6ZDV/9meHJMtc0cSupg58Zze/Lm5LNZe89FxwwirW3VVsOyrGb/CUpLS3E6nbhcLhITE6PdHBGRVsFnWpz3p3+Aaw8r7PPoYSsMvpZjdqWE9nS2uUn/7XvEtKu2t4wr198b0qErXLYQEtP9wzlSZ3Xa7bgWdd6gsBmo6/NbYUREpI1at62Ya1/8FwADje9Z6ZgTfO1Kzxx+sDLowEGenHxpzVkurj3+3hCFkCYVWDTtyAd3IMY0t3VZ6vr8rtcwzaJFi+jZsyfx8fEMGTKE9evX13puZWUlDz74IL179yY+Pp7+/fuTlZVVn7cVEZEG4jMtvtjqL0xNp5gn454Nef3JuGfpyEHySQo/JdXZXUGkiTXkBoXNTcRh5K233mL69OnMnj2bjRs30r9/f0aMGEFhYWHY8++//36ef/55nnnmGbKzs/ntb3/Lr371K7788svjbryIiEQua3Me5/3pHyz8bBvpFAeHaHaZKVzpmcMuM4UetkJW2OeRTnGbWeuiuTvWomktedXWiMPIE088weTJk5k0aRJ9+/Zl8eLFtG/fnqVLl4Y9/7XXXuPee+9l1KhR9OrViylTpjBq1Cgef/zx4268iIhEpvreKGlHBJGx3llstE5mrHdWMJC8Hf8Qg5Nq7jsjTa+ui6YFzmtJRa4RTe31er1s2LCBmTNnBo/ZbDaGDx/OunXrwl7j8XiIjw9N1e3atWPt2rW1vo/H48Hj8QS/Ly0tjaSZIiISxpHd/BW0o5hEMGGsd1ZwWfc8krjWO4s37fNwJqcRE58QvUY3seMpLG1sde2hSkmIb1FFrhBhGCkqKsLn85GamhpyPDU1le+++y7sNSNGjOCJJ57gZz/7Gb1792b16tWsXLkSn89X6/vMnz+fuXPnRtI0EZG2LcxKqoEHa2nhTjp36kJlXELIw6mM9kzwzqDDodqQ6ixnd7b//C8MO6NXm6kNae4P8GMtmhZYtXV/hZepb9Qscs13uZmyfGOzK3KFJlhn5KmnnuKkk07i1FNPxW63M23aNCZNmoTNVvtbz5w5E5fLFfzKyclp7GaKiLRcgZVUl406tP7H4bqQ6S9+xKn/M5bYN6/mzlc+r3FpGe1rBJFpw/qw9p6LGDb4zDYVRALDV9UFHuBZm/Ma7b3rOpwSWDQNDs+eCQh8P+vS05j3Ucsrco2oZyQ5OZmYmBgKCgpCjhcUFJCWlhb2mq5du/Lee+/hdrspLi6mW7duzJgxg169etX6Pg6HA4fDEUnTRETarmorqbLsUtYMfZkpK/NCakIwwVZZDhz7/63n9kluNkMTTeFYs1QM/A/wi/umNfh9ibQ3JrBo2pHXBDYodLaz17nItTltShhRz4jdbmfQoEGsXr06eMw0TVavXs3QoUOPem18fDzdu3enqqqKd999l8svv7x+LRYRkVDO7v7ddTv3hP076bNqLGceWkm1enHqkT0gRzLwPwjbyuZsAdGapVLf3piR/dJZe89FvDn5bJ4aOyBk1dZIi1ybi4j3ppk+fToTJkzgpz/9KYMHD2bBggVUVFQwadIkAMaPH0/37t2ZP38+AP/+97/Zs2cPAwYMYM+ePcyZMwfTNLn77rsb9pOIiLQVbheU5vl31A3UiDgzYOJHuJ+/mIwDPwYXMAsEkbw6BBFoW5uzBUTjAX68vTExNiNsz0YkRa7NScRhZMyYMezdu5cHHniA/Px8BgwYQFZWVrCodffu3SH1IG63m/vvv5/t27fTsWNHRo0axWuvvUanTp0a7EOIiLQZbhe8egUUfAMdu/r3inFW2zHX5wk5/c7K3x0ziMDhbv7mVtjYFKLxAI+kN6Z66DjWbJ+6Frk2t96veu3aO23aNKZNmxb2tTVr1oR8f8EFF5CdnV2ftxERkYDAbBnw14f4PP5i1aUj4JpXwN4BXvsV8Z7ikMuejHv2qD0j44f24JJ+6c1qCmtTi8YDvD69MXWpLznazsDNufdLu/aKiDR31WfLYMGNWf7dcsEfSJb8AhafB2X+GoMqbEz23FljJdVwLumXztDeSc3u4dSU6jJLpaEf4JH2xkRSX1LbzsBpzvhmOa0XtFGeiEjz5nZB0VZ490b/bJnOPf3FqgAvDIOKI7biMGJYd+EbXPs/vhpLvY+pVsQa+Nf+2nsuatNBpLqmXGcksGPysXpj1t5zEQDn/ekftQ7r1PZ72RwWcKvr87tewzQiItIEAj0iFXvhqpfg3ZuD03f5xUNwIExvx69fYehpI1mclMeMlV8z9sAsVtjnUUwiFbQDmnd3fTSN7JfOxX3TmuQBHslwyrptxfWqL6mtyLU5UhgREWmuqq0fYr17M18PeZzen99Bh/074a1x4a/JmgHdzmRkvwwu7pvGwn9sZfLaB8lzx1JGe6BtF6seS0M8wOvaI3GsNUMCvz8tdbpuJDRMIyLSnLlyOfDCSNpX5LDLTGFx1S+Zbz9iY9KENMAI1ozgzAiZZdMcuuvbivoM9Rzr92fdtmKuffFfx3zvNyef3ex6Qur6/FYYERFpDo7YWybwgPp7dj7/98XfWWh/mhNsRTUuM7Fhu/kTSEiHpSPBdWj7DGcG3PhJyF410rgCRaZHPlQDsaK+xaOR1Jc0t5BZ1+e3ZtOIiETbEXvLBPaVufbFf7Hqiw08Y19I5RGj6pWWjUrLwIaJ9e7N/oOBWTYxDujQFRwdo/Bh2qZjLWIG9d8TJhqzfZqawoiISLRVqw058MJIHlz+KXkud8hsmF5GfsglhZaT33ims8tMwQgUtWL4h2d+8zmMf7/NbHLXHDT2kvItcbpuJFTAKiISbYf2lrGWXUr7/Tt50z6POyt/x5Nxz9LDVkilZRBnWFRaNn7nvZ37416nh62Q2fbXuM07jTe6vEiHQE+IAkhUNEWRaVPO9mlqCiMiIk3tiPoQAJwZbBy2nNR3rqCHrTC4t0ylZSPOMNltJjPNextf0YfN3l7BHpOn7QvZ9rPnOWPAIAWRKGqqJeVb0nTdSGiYRkSkKR1RH+IzLdZtK+b9TXv4z879xOILOX27lX5owbLZfEUfAPJIYqx3FrvMFEpjOvGTMwYqiERZYEn52voo2uqOyHWlnhERkaZ0RH3ItZWz+G9pR9Ip5m37HNJs+0NOb4+Xqd5ba+wtk38okMwbczant+/UhB9Awmmpe8I0F+oZERFpSofqQw50yKR9RQ5Pu+9noPE9b9vnkGHzr6iaayZzpWcOu8wUMm17eca+sMbeMmnOeGaPu5jhZ54cjU8hYbT2ItPGpHVGRESamM+0uPKPf+Fp9/30sIXuLZNrJnONdzZ5JIXdW2bUuYO4uG9aqylcbI20yNxh2ptGRCTawhWq4p8GWlDq4T7jRpY7/hg8nm91DgYROFwbssI+j9KYTjw05mz1hLQArbXItDEpjIiINIbqm9xN/Ci4NDtAacEO3rHPpatREnJJlRU6cj5tWG9OSh1AkW0gA/pkqDZEWi3VjIiINCS3C1x7QgpVWXYpuHL9xwu/5cIvbiDDVoTDqCLXTArWh2TY/MMygfqQc/t05fIB3Rl0Rj9iFESkFVMYERFpKNWn7WLhG/833B1PgP078bxwMdZLF8HzP8NR8SMAuWYS13jnsNE6OThVt4etkBX2eZyRWK5poNJmKIyIiByvML0hFS+M5JfPrOXXRTezx+yCo+JHjLJ88Hkhxs7Bdun82juH/CPqQ3aZKRSTyG2XnNlmix6l7dFsGhGR41GSA29d7w8kEz8CYP+zv6CzZw95ZidsBnSmDLtxeDGz/zv/Jc766Tlk5dhqbDd/RmI5t11ypgpVpVWo6/NbYUREpL5KcuD1a6D4BzCroHNP/jFkKfe99w3v2e8j1VYa9rJcUkm/fTUxnTM1DVRatbo+vzVMIyJSHyU58MavDwcRWyzs38lJWdfyS+MLUozQIJJvdT5cqEoBlUv8y8EHpoFePqA7Q3snKYhIm6QwIiISKbcLVlwPRd8fDiJmFaYRSyYF3OdYgXFEpqiybORZh+tC4st3H5plsyc6n0GkGVEYERGJlKccPK6QIIItFptVFXJapWUj3+pMrpkUnLYLMNY7yz/LpkNXcHSMxicQaVYURkREjqUkB3I3HP7+0P4ydO55KIjE+P97hL1WJ27x3Mk13jkh03Y7xscSd/P/wLh3tduuhOzcvG5bMT6z2ZdyNjitwCoicjQF2fDSz/1Tcm/Mgoyz/MedGXD+7+GDaWD6alxWZdnoZtvHM/aFjPXOCi7rXkwiP+/fi5hOGTWukbYna3NejRlV6c54Zo/u26Y21lPPiIhIbdwuePdGqDzg7/lYOhJy/8//2ncf+YNIGFWWjVjDpNKyBXtDLGCMdxYTvDO44IyTmu4zSLOVtTmPKcs3hgQRgHyXmynLN5K1OS9KLWt6CiMiIrXxlEPlwcPfBwLJ54/DiuvCXlJFTDCIxB36byntqaAd+STR0dlFK6u2MI0xjOIzLeZ+mE24nxQ4NvfD7DYzZKNhGhGR2gRqQ5Zd6t9jBvyB5B8P1jz353Nh48vE7t8ZEkS2Wt24xTudMtpjALNH99X03RaksYZR1u/YV6NHpDoLyHO5Wb9jX5vYAVg9IyIi4B+SyflPaKEq+GtDrloCHVPDX2eLhbFvwvl3BIta4wyTKmLYZnXjRu/d5JFMujOe58YNbFN1AC1dYw6jFJbVHkTqc15Lp54RERG3C176BRRtAZsNbvz4cKGqK9e/yurBfeGv/eWTcOoo/6+dGcGelBiHk4qhC5lhJWll1RboWMMoBv5hlIv7ptXr93Vn0YE6nZeSEB/xz26JFEZEpG1zu2DnF1D0nf970wdLR/gDCQYsvxrc+2u//m93QsppobNsJq7CcHRkULyTQY3+AaQxNOYwis+0eHP97mOel5boaDP1RQojItJ2uV2w/Cpw5YARA9ahKbqmD5b8Aiyz5jWjn4K1T4bWkCwdCTd+AhmHooeze5M0XxpPYw6jrN+xj/zSY1937eAT2kxvmmpGRKTt8pRDxV4oy8ftSMY0qv0vMVwQGfsGDJp4eMGzgBg7dExp7NZKE6rr8Eh9hlHqGmB6JneI+Ge3VAojItJ2ObuzZujL5JJKvLuAAl8iPivMv0TjOsK1K+DUSw9dl3E4kKScBjevhk6ZTdp0aVyDT+xCujOe2volDPyzauozjNKYQaelUhgRkbbD7QrZmC5rcx6TVuZxjft+cs0k0m0lxBhhSharDkKH5NBjh2pDuPFjSO3byA2XphZjM5g92v/7emQgCXxf32najRl0WiqFERFp/dwuKPzOXx+ybBS4coOzJVIppgMHsRs195YJsg4VtQZWXw1wdtfeMq3YyH7pPDduIGnO0B6KtOOcpt2YQaelMizLavbLu5WWluJ0OnG5XCQmJka7OSLSkgSKVMvyAMNfrNq5JxuGLWfaG1/ytn0OKUYJdiN0f5kSM56OhpdYo1rtiC0Gbvz0cKGqtAk+02L9jn0UlrkbdJp2W9iXpq7P73rNplm0aBGPPvoo+fn59O/fn2eeeYbBgwfXev6CBQt47rnn2L17N8nJyVx99dXMnz+f+Pi2Mx4mIlHgdkHRVn+RqivXP7TizIT9O+m76kres3tJtZWEXGJZUGR1pKutnDyzE6m2UmyBYlZbnApV26AYm9Eoq6CO7JfOxX3TGiXotDQRh5G33nqL6dOns3jxYoYMGcKCBQsYMWIEW7ZsISWl5l/SN954gxkzZrB06VLOOeccvv/+eyZOnIhhGDzxxBMN8iFERGpwu7BeuwqPq4B/D/wTQzbeQ7xrtz+QdEihXUUh7aoNVJsWmBjc45nMPzmdFfZ59LAV4nakEu8pgpg4GP+hClWlQTVW0GlpIh6mGTJkCGeddRYLFy4EwDRNMjMzufXWW5kxY0aN86dNm8a3337L6tWrg8fuuusu/v3vf7N27do6vaeGaUQkUp+t/5KTVo0hgwJ2mSnc5p3GovhFZFBQ49x8szO/8d6BicHX9AGgG8X8Jf4hunc/AePns6BLLwURkQjV9fkdUQGr1+tlw4YNDB8+/PAPsNkYPnw469atC3vNOeecw4YNG1i/fj0A27dvZ9WqVYwaNarW9/F4PJSWloZ8iYgck9uFd18Od/1lE5NW/sg17vvZZabQw1bIIvtTvOS9OOxlVcRQSJdgEDGAPJL4YdRbGDe8C70uUBARaUQRhZGioiJ8Ph+pqaEbRqWmppKfnx/2muuuu44HH3yQ8847j7i4OHr37s2FF17IvffeW+v7zJ8/H6fTGfzKzNT/BETkGNwucp8ZRd6Ci/jnxq8Af6AY651FrplEhq2YOfblodckpHGwfTcybEWssM8jnWLg8GyJYYPP1GwZkSbQ6FN716xZwyOPPMKzzz7Lxo0bWblyJR999BHz5s2r9ZqZM2ficrmCXzk5OY3dTBFpqQ6tHfKH19fiKyukh60wGCzSapm2W2wl4HKkQlk+7eJsWM4MetgK+XuXP/PudT1Ye89FrWY2g0hLEFEBa3JyMjExMRQUhI65FhQUkJaWFvaaWbNmccMNN3DzzTcDcPrpp1NRUcEtt9zCfffdh81WMw85HA4cDkckTRORtuhQkWpRQS5ry+9lLbOChadv2+cQa5h0obTGtN2Dlp3fH7yVN5Newti/E+PQLJsOCWkMOjkT2uBsBpFoiqhnxG63M2jQoJBiVNM0Wb16NUOHDg17zYEDB2oEjpiYGABawBInItIcHeoN+cembeTk7qJrVR4r7P7e1lu909hjdibDVkyasT8YRCotg9s8vwsO2fyJp/l6yOP+Jd0T0uH6d2DcuxqWEYmCiKf2Tp8+nQkTJvDTn/6UwYMHs2DBAioqKpg0aRIA48ePp3v37syfPx+A0aNH88QTT3DmmWcyZMgQtm7dyqxZsxg9enQwlIiI1Mnu9bB9DdY3KynaX8J95fdCtd6Qj+wzqCKGTkZFyGX5ppNbvHfxFX34P+9pwfMrPr8DrnsFkvsohLQgjbUImURPxGFkzJgx7N27lwceeID8/HwGDBhAVlZWsKh19+7dIT0h999/P4ZhcP/997Nnzx66du3K6NGjefjhhxvuU4hI6/fqNbD9EywAC7oasMI+j7HeWdi9B7Ac0MVWEfbSKuLYS2fgcFHrCvs8urRPVhBpYdrCqqVtkZaDF5HmbfV8+H9/DIYQ49A/gK1Dv/aaEGccPh6Qb3bm95W/4Y9xL5FhK2KXmcJY7yzy8C8wdUZiOX+9YwQx7Ts15aeR45C1OY8pyzdy5EMr8Ft/PPvFSONolHVGRESa1Jwu/iBiERJEwP9rywK7rWYQAf/aIdus7lzjnR1ca2SFfR5ph6bv/vaX5yuItCCBjQ3D/es5cGzuh9n4zGb/72sJQ2FERJqnhzIBX7AHJFzgOPJYleXvEck1k4NrhwCM9c5il5lCMYlU0A6Azh00Y68lWb9jX8jQzJEsIM/lZv2OfU3XKGkw9dooT0Sk0bhdsOdLDlaVEm+FDyG1KbS68FvvHeylc7BIdYV9HmO8sxjjnUUF7Sijvf/cstofbNL81PX3S7+vLZPCiEgrd8BbVetrNsMgPi6m0c896PVhhe1gBwODdvZD5xZkc/D18Ziu3WA52O8Fh/1wIDGwaGd4g9e6rTjMQx28lgWdjAr+HPcCEyrv4dfeWfzFPi/YGxIIIQGJ8XEhn6G9/fD/Dt2VPsyjlNNFcm67uBiMQx/AU+ULGUao/nPk6FIS6rbLe13Pk+ZFfxNEWrm+D3xc62vDTunKy5MGB78fNO/vHKz0hT13yIldeOs3h9cTOu9Pn7Gvwhv23DMynHww7bzg98Of+F/2lBwMe+5JKR35dPoFUJIDL1zIZQfm8YOVcfiEam/Rnb18EX978Ptfex/gK6t32J9rYNboDalu0rL/C/66XVwM384bGfx+yvINfLZlb9ifC7Dzj5cGfz39L5tY9XX47TAAsh8cEQwd967czLsbc8P+HDm6wSd2Id0ZT77LHTbWGviX8R98Ypembpo0ANWMiEh0mZXw1TvwrxewfJ4G/MEG+SSFDSLS8sTYDGaP7gscnj0TEPh+9ui+Wm+khdLUXpFWrlkP05TswVh8Hu3M0uDUXTd2rBqPG7+jDdOAf6jGDVzjncd2Mmpcn5ro4N5Rp3Jx35rbV2iYpmXQOiMtS12f3/qbINLKRfLAa6xzgzUhR9r3DZZZCvj/dWsB8XiPWrRqVStqjTcqQ45jwAOeG0OCSJcOcfxqQHeG902r80qd1YNUQ57riNWq08drZL90Lu6bphVYWxmFERGJjpIcqt6+CSwbYBJrHF47xKplFo1lgccDDkfo4mcB8zy/5l2Gc/XADNo7YujRpT03DO2JPVYj0q1JjM1gaO+kaDdDGpDCiIg0OW+VyaL31jKtyk2cYVJl2aiyDgeS2hiGP4gEAklAmQduYA5fcTKd2sfxTrUi0ZfW7lAXvkgzp5oREWkaJTlQXsj8r9rx/Oc7AOjPVt5xzCHOMPFZBjFG3f53ZFngPlTr6gbO5I1jXnPn8JOYdtFJ6s5v5uqyCZ42yms5VDMiIs3H1s9gxVh8VZX8yz0b6APAf+nDK56fc5Pj07BBJPBPpeq9JZYFJgaj+SNbyaxzE578+w+8uT6HOZepl6S5qktxqgpYWyf1jIhI4ynJgd3/hpU3cai+lErLxtWeOZjAcP7DbY4PagzNVK8ZqR5ILAsKrAR+472L/3JyvZpkoA3VmqO6bIIHhD0nQL1fzU9dn98KIyLSOLZ+Bm+OBZ8H69Bk3UDI8Fn+mTMxhK8R8VkGFhaxRwQSE7jSM6feQQQOL4619p6L9NBqJnymxXl/+kete88Efs8syyK/9Ohr0aQlxqv3qxnRrr0iEj0F2bD8V+BzAxZV1WbIWBbEGITMngH/f9/3DqYqWDtiC15nYnCfZwIjPH86riAC2lCtOarrJnjHCiIA+aVupizfSNbmvAZsoTQ21YyISMMpyYF928GVS2Bjd8uCOMO/o25MmCm7hgFVlsEjnrEsZTRLPYeLWqssG096RrOKcyKqD6mL6huqqSAyuhpjc7u5H2Zzcd80/T62EAojItIwCrLhxYuwqg7iiU/hYW7hQeuFYO9HbC3PBMuCGZ6beYdhgL+o9WrPHN5xzMFDHG/zc/JIbvDmBjZUU0Fk9DX05nbVe7+0HknLoGEaETk+bhdsWwMrrsOqOogBxLsL+Z35Fx7yXFfrAmZweOhmvmMJ/dkaPP5f+nClZw4Xex5t8CBi4A8bg0/sEiyaPHKIIN+lrv6mFNgEr7Y+jMDvWVqio9ZzwmmMHhdpHAojIlJ/BdnwwkXw2uVY+3cEi1QB0m0l3OsIv/7HkTUkcYbJO445nM5WDMAZH8vX9GmUHhEguOHa3A+zw87MCByb+2F2yF4y0jjqugnenMt+EtHPbegeF2k8CiMiUj+HhmXY5+/RqD5bJhBIYsKsqBoYsqk64nwfMRTRiVcnDebG806sd7N+eUY66c7wD6F0Z3xwWm9diyZV6No0RvZL57lxA0k74vcurdrvWfCcREctP+Wwzu3jGHxil8ZqrjQw1YyISORKcuAvN0DVQYDgGiKBYFHbvjJQvTcEKi2ItfxTdid57iKPZG5d8SUlBytr/oA6SIiP5amxZwL+GRr5roPsq/DSpaODtMTQwtS6duGrq7/p1GUTvJH90rno1FQGzvuUck/tO0erP6tlURgRkci4XbDieti/M3ioeq9IbfUh+6z2zPdez6OOF4OBpIo47vFM5Cv6BGfL1DeIAGR2bhd8cB2rcLGuXfjq6m9addkEb8Ou/UcNIgAlBypVwNqCaJhGRCLjKQePC8wqsPn/PWNx9CJVgCTbAe6yv80fPJP9O/NicKPnLlYyrMGm7e4sPlDnGo+6Fk2qq7/5Ua9W66MwIiLHVpIDuRvwmRb/L9/OnC5/Zm9sOphVWNhqfaAfWUOSbivhLvvbTPfcwi88f2QdZzRoMw94fXWu8ahr0aTWqWh+1KvV+miYRkRq53bB3h/gtcswK91c55vLv729ANjIFF6wP06azVXjsupDNtUDiQXsxcnfGUwZ7YPnd2oXd1zDM9VF8q/hQEHkkeuMpGmdkWYt0KuV73KHrQ0JLB+vXq2WQ2FERGpyu2DPl/DxDDhYglnpxmb5WG48wG+4k3g8zLa/SopRWuPS6uGjeiDZZaUww3sT39CbMtpz+89PolfXDqQkxGNaFte/9O8GaXqk/xquS9GkNC+BXq0pyzf665WqvaZerZZJYUREQpXkwPKroOh7Av+bLzQ7k2y4iDNMljgeB8LXiJgY2AyrxvDMQezc7L2LrWSS7ozn0SN6HXymddR/6dZVfWs86lI0Kc2LerVaF+3aKyKHleTAq1cE1w6Bwz0cxWYHuhgVYdcNedl7MZfE/Yd0235MIwab5cOywI2duzyT2dmuH2cP7M/FfdNq7XUIrIYK9ZuWaUBwPQppO7SvUPNW1+e3woiI+JXkwPIrD/WIhKpt7ZAqC+7x3MK7XEg6xXyUOJ8u3h+xbLEc7JjJukELaJ9xep0fEOH2iUlLdPDrn2byxdZCNu4uDRtUtJeMSPOkMCIided2YS0bjZW/GRu+Ol9WZdm4yjOH/9IHgEcu6sx1304BhxPGvg6dIp+ye7R/6QZeO9piZiLSfNT1+a2aEZG2bPd6KP6Bzyr7clJ+Phn4qLQM4oyj/xtln9WRRA4Qe2hPmasPBZLMHifBkFXg6Ajxzno16Wj1G6rtEGmdtM6ISFu1ez3W0oux3v8dH618nWvc97PLTCHOsKg6ShaxLOhilLPXclJl2UI2udt30AvO7vUOIiLSNimMiLRRm778P8Bf+Pmo40XO5SvGemex10wg9igjHoFZMum2/cFA4iGOIjppkSkRqRcN04i0JW4XlOax8J/5PPavnlzN5OBeMY86XuTvvv4kG2XH/DGBQFJCB+7wTCGHNHBmaJEpEakXhRGRtuBQCOG9KfjyN3NFVQKvM4d3GIbTU8H9jjcwDLg49r81LvVZEBNmOu9B4rjVeyvbDu0r85wWmRKRelIYEWntCrLh7YngcWFhEGN6ybAV87Z9Dk96r+QW+98wMYg5YtKsz4J9VgJdbWUhRa2WBTutrkz2/j64iJmm1YrI8dDUXpHWbOtn8Po1YPn3fXHHp7D/QCXptv1A7euHWBY87bmMFVzMCvs8etgKqbQMLGxst9LZcN7zdEw5UYtMichRaWqvSFtXkA3Lf0X19Uzj3YVAJ8qteDoa7pAgEhh6aUclhgG3OT4gx5PKWO8sVtjnUUp77vdOoiKxFx8PP1cBREQajMKISGtVshsLK7iRWCA6pNtKwp7+gvcSXrZGcR5fhRS1TvfcwhjvLA7QjjLa89xlAxVERKRB1Wtq76JFi+jZsyfx8fEMGTKE9evX13ruhRdeiGEYNb4uvfTSejdaRI7C7QLXHrIq+/OH2Bn+oRgOb1pXm0vj1mMA7zCMP3gmB8/fTjfySaKDs4v2fhGRRhFxz8hbb73F9OnTWbx4MUOGDGHBggWMGDGCLVu2kJKSUuP8lStX4vV6g98XFxfTv39/rrnmmuNruYiECsyY+WAaB/bn82Dx3fzIGeznLl5yPB62NsRnQQXtSDQOBotar/H6Z9n4PAYXnHsON2YOVm2IiDSqiAtYhwwZwllnncXChQsBME2TzMxMbr31VmbMmHHM6xcsWMADDzxAXl4eHTp0qNN7qoBV5CiqhRDK8vxDMq5cdpkp3OqdxgHied9xLx2MqrCX55mdACNY1JprJnGNdw44u7P2nosUQESk3hqlgNXr9bJhwwZmzpwZPGaz2Rg+fDjr1q2r089YsmQJY8eOPWoQ8Xg8eDye4PelpaWRNFOk7XC7YPlVUJYPWODKxdO+G0VmMj1shax0zAHMGiuqVp9Fk24rIc/sRJ7ZmS5GGUUkUkE7/qx1Q0SkiUQURoqKivD5fKSmpoYcT01N5bvvvjvm9evXr2fz5s0sWbLkqOfNnz+fuXPnRtI0kbbF7QJPuf/XFXvBlQPOTHBmEO/KJRb/Mu2xhhly2QHr8GyZ6oEkyShjvOce9tIJIzGdP//6LNWGiEiTadLZNEuWLOH0009n8ODBRz1v5syZTJ8+Pfh9aWkpmZmRb0Uu0iqV5MCbY/2B5MYsmPgRLLsU9u+EDimY2EizuWpcVmY5+IXnMfqyM1hDYln+mTauDr244ZeX06VLV9WGiEiTi2g2TXJyMjExMRQUFIQcLygoIC0t7ajXVlRUsGLFCm666aZjvo/D4SAxMTHkS0TwB5HlV2EVbAZXDq5FP2fuB9+wuOfTeBzJUFGIDTPspS6rIwCrGcTNnrv8QcQw2HTWY3S9bTWXnnUqQ3snKYiISJOLKIzY7XYGDRrE6tWrg8dM02T16tUMHTr0qNe+/fbbeDwexo0bV7+WirR1Bdnw2q8wi7cG1wxxevO56YffUfmvl7C7i8Jets/sQK6ZTIatmBX2eaRTzGoG8XH/p7B++wUDfzkZ4p1N9zlERI4Q8Toj06dP58UXX+SVV17h22+/ZcqUKVRUVDBp0iQAxo8fH1LgGrBkyRKuuOIKkpKSjr/VIm1NSQ68dBFW8Q/YLB9V1uG/uhm2Im51fFBj6m6VZSPP7EQXWwVgkXuoqHWFfR5nJJZz8RUTiEn7SdN+DhGRMCKuGRkzZgx79+7lgQceID8/nwEDBpCVlRUsat29ezc2W2jG2bJlC2vXruWTTz5pmFaLtDXlhVi+yuDiZbGGGbZAFcBrxWAzLGINE58VS66ZRIatmFwziVwzmWISue2SMzUcIyLNhjbKE2muCrKhZDecMhKAr/71d077n2uIM8xaN7jzWTbGeWZQQTyvdnqeTu495JrJgEUhnfmz41ZuHnUOw888uWk/i4i0SXV9ftdrOXgRaWQF2bD4XHhzDHz3EQA74k/jas8cKmsJIgAxhsmj9hfYS2f+/bNXsTr3xJmSyZbhL1N17Tu8PnOCgoiINDvaKE+kOSrZjWWZ/mGZFdfx3QXPk5wxnGRcYf/SVlkGsYa/kzPDVsTb9rkUdHwfY+IqEhwd+bkKVEWkGVPPiEhzcWiDO6DGJnenrvkNOW/cykvxNfeY8deQWCFFrSm2EgakxoCzu2bKiEizp54RkeYgsKx7xV7WDH2ZKSvzsI7Y5G6stSp4umVBqeWgg1FJ7KEakkBRqw842OkkOjm7Re/ziIhEQD0jIs2Az12Gu6QA9u+k96qxpFEMQDY9KaV9yLkW8LAxiRHex7jKM4dKyxZcTTXHSOM/w96k05SP1SMiIi2GZtOIRIPbBXu/B8PGqn3duP/9zTgq8lhhn0cPWyH5ZiceqJzIQ3Evk3LE0u6WBd9esJj9mRfzz+1FxP64gdt23YoVE4cx+R9aO0REmo26Pr8VRkSaWkE2rLgW9u/Ch8GV7jn8lz4AXMVn/NnxEjFG6F9Ly4LlVRcxLvYf/l4QwBj7Bpx6qf+E3A3QMQU6aQ8nEWk+NLVXpDkqyIYXLvRvaodFDCbvOGbTn61cxWc85ngxbBC52XMXs3w3B/eUMQBWXAdbsvwnZQxSEBGRFksFrCJNwe2C0jx490bweUJeijMs3nU8QAzh1w/ZaznJpifg3+TuD3EzeLTqjxiGDTqd0PhtFxFpZOoZEWlsJTmwbDTW61fjLvfXfxw5NhprhAaRIjOB2zxTKDSdpNhcwQ3uAIZfMRHj2rfgt19Aat8m+hAiIo1HPSMijakkB974NebeLdgsH0VmMuDfK6a2Jd1dVntGex8hjyT+z9s3WNS6wj6Pv/Z/gZH90oH0pv4kIiKNRj0jIo3F7YIV1+Mr9AeRSstGhq0IMCgyE2pd0r09B0hhPwB5JDHWO4tdZgrFJHJO355N1nwRkaainhGRhhaoD/FWUFG2jw74g0jcoUXJ/IGkdnEGvOOYzdWeufyXPuSRxBjvLOztE/jslJ5N8xlERJqQekZEGlJJDrw8Cuv5n+F+43omlt7CLjMlGERiDTPsZUdOsI8zLN5xzOZ0tgKQTxIXn3kyMbZaulNERFowhRGRhnJoWMYs/BbD5yH+QB5PGgu4zTuNPLNz2CBiWbDP7BBcQbX68UpiKaJT8NjwvmlN8CFERJqewohIQ3C78O39gb3Fe7FZvuCmdRm2YpbYHyXF2F/jkkAB6wHakWsmBQOJZYGbGK713EceyRhAujOewSd2aeIPJSLSNFQzInK83C5KXhhNWXE+0zxTedq+kB62wuCwTLKtrMYlPssgxrCCRa25ZjK5ZhJltOMZ7+V8ySnBIAIwe3RfDdGISKulnhGR4/TZV9txFeWRaRTwtH0ht3mnsctMCTss84J3BLlmUjCIxBkmlZaN/XRkgvcefu2dwyrOJY9kANKc8Tw3buCh6bwiIq2TekZEjoO3yuS3H+TTpWpWcD2Qp+0LebPqQmbY/1Lj/FGx/2Gq9/Zg70mlZWOb1Y1bvNODAWTWpaeRnOAgJcE/NKMeERFp7bRRnkg9ZW3OY+bKr9l/oBKAdIqDgeRIgWEZgFwzKRhISmkfDCIG/p6QtfdcpAAiIq2CNsoTaURZm/OYsnxjMIiAf4GyRZWXhZxXYrUnz+xEjGGFFLUusj/FNO80rvPer9oQEWnzFEZEIuQzLeZ+mF1jf5kz2MrD9iUhx8qt9vzWOz1YQ1Jl2agy4nDZOrGTbpTRHlBtiIi0baoZEYmAz7RY9sUO8lzukOPpFLPI/gxxhkWlZTDPewO3xK0iw1YULGp9xrEIZ+cuOK9ayKlJfXghz0dhmVu1ISLS5qlmRCScgmwo/A5OGALO7oB/aGbuh9mc4voCN3a+oTdltCeNYt46VCuSY3ZlqvdWvqJPSA1JiaM7CeNeJabrSRDvjPKHExFpGnV9fqtnRORIu9fD0ov9v+6QArd8RlZODFOWb+Qq1vCo4wUAvrVOYIz3ASpoRzGJYMJY7yzySAIOb3K3yvlHOnftBgoiIiJhKYyIBAQ2uHv/d4ePVRRivTCMv3incx3f8pDjleBuu8mWiw4cJJ8kJnhnBH8dYDPgsvN/SudzPwVHRwUREZFaKIyIgD+IvHo5lOWBLS7kJaOikBetGdgcBINIkZnA5d6HguGjjPbBYlSAqwdm8MiVp2OPVY24iMixKIyIuF3w4yasgmwMn4eDjhRsHbrhqPgxeEpMtdrSIjOB0d5HgsMx1SV1sPPwr/ppVoyISAQURqRtK8mBt67nYEkhZb4OpOChnaeQvIOdiLcl0pnSGpfcUnlX2CACcO8lpyqIiIhESH3I0ja5Xf7ZMiuux8zfTLuDeXhNgzyzMwDptpKwQQRgcdyTpFMc9rWSg5Vhj4uISO0URqTtcbtg+VXw+tVYB4uxWb5Du+cWAxY+K/x6H4FJ8Ck2F+/b7w8bSLp0dDRiw0VEWieFEWl7POVQsRdcOXirTHLNpEO75xqk20qCe8gEVFmQZ3bGMGoGkrQjAklaYnxTfQoRkVZDYUTaDrcLXHvwJXRjw7DlVLTPPFSkalBoJhJnhF//L9YAsMg1k4OBxLKgCCcVtAuel+70r6QqIiKRUQGrtG6BtUMAPphGxf58rj54L98ecHIGk3nO/iQZtqKwl5aY8XhxkGJzkW4rIc/sRK6ZjJs4HvaO5T/8hDLaa5M7EZHjpDAirZfbBUtHQdEW6JBMicekk7eAxeZsbmMaL9gfp4tRFvbSSgs62dzkmh3IMzuTbttPF6OcCZ67+YZeIWuKpDnjmT26r2bRiIjUk8KItF471kLhZv+vy/I4aHamnCR62ApZ6XggZO2QAJ9lUGg5SbeVBItac80k8szOFNI5JIjcdG5PhvdN0yZ3IiLHSWFEWqeSHHj3ppBD6bb97LWc+CxqBJEqy6DIcpJmK8FnxZJrJpFhK6bSsrGfBO70/o4CulBGe9LVEyIi0qAURqR1Ki8E07/mhwXBuo6uhqvGqXvNRG7y/p69dA7usptrJpNrJrGfBG7xTufmS88nOcFBSkK8ekJERBpYvWbTLFq0iJ49exIfH8+QIUNYv379Uc8vKSlh6tSppKen43A4OPnkk1m1alW9GixSJxmD4MYssMVicHhKbjge7Oylc3CX3V1mCoV0YoL3Hq733g/ODCaeeyKXD+jO0N5JCiIiIg0s4p6Rt956i+nTp7N48WKGDBnCggULGDFiBFu2bCElJaXG+V6vl4svvpiUlBTeeecdunfvzq5du+jUqVNDtF+kdhlnwY1ZmC+NwGb4arxcbCVw0LKTYStihX0eY72zyCOJMd5ZVNCO8kO1IX/WLBkRkUZlWNbR/s1Y05AhQzjrrLNYuHAhAKZpkpmZya233sqMGTNqnL948WIeffRRvvvuO+Li4mq8XhelpaU4nU5cLheJiYn1+hnSRrlyqXxmMHFVFWFfzjM748NfqLrLTGGMd1ZwJ17VhoiIHJ+6Pr8jGqbxer1s2LCB4cOHH/4BNhvDhw9n3bp1Ya/54IMPGDp0KFOnTiU1NZV+/frxyCOP4PPV/JdqgMfjobS0NORLJGKuPXgWnlMjiFSP3+m2/cTgX4W1mMTgImazLj2NtfdcpCAiItIEIgojRUVF+Hw+UlNTQ46npqaSn58f9prt27fzzjvv4PP5WLVqFbNmzeLxxx/noYceqvV95s+fj9PpDH5lZmZG0kwRAHzffoS90l+walnwhvcCqixbyLLulgVltGeC9x4meGdQfmi2zMRzT9TQjIhIE2n05eBN0yQlJYUXXniBQYMGMWbMGO677z4WL15c6zUzZ87E5XIFv3Jychq7mdIC+UyLdduKeX/THtZtK8ZnVuvyyN2A7eMZweLVhzzXcq/5G67yzKGyWiBxE8PvvLexjYxgjYhWUhURaVoRFbAmJycTExNDQUFByPGCggLS0tLCXpOenk5cXBwxMTHBY6eddhr5+fl4vV7sdnuNaxwOBw6Hdj+V8HymxcJ//MDLX+yk5GBl8HhIjUfHFHw2B1S5ucdzE+8yDID/0oerPXN4xzGHKmIZ67mXrfh73rSSqohIdEQURux2O4MGDWL16tVcccUVgL/nY/Xq1UybNi3sNeeeey5vvPEGpmlis/k7Yr7//nvS09PDBhGRo8nanMeMlV9TcqCyxmv5LjdTlm/kuXEDGdkvk/9e9jFz3vyMr+kTct5/6cOVnjkU0Yk8kpk2rDfn9umq9UNERKIk4mGa6dOn8+KLL/LKK6/w7bffMmXKFCoqKpg0aRIA48ePZ+bMmcHzp0yZwr59+7j99tv5/vvv+eijj3jkkUeYOnVqw30KaROyNucxZfnGsEEE/IubAcz9MBufaTHg9NMpcvYjXLz4mj7kk0y6M547Lz5F64eIiERRxOuMjBkzhr179/LAAw+Qn5/PgAEDyMrKCha17t69O9gDApCZmcnHH3/MnXfeyRlnnEH37t25/fbbueeeexruU0ir5zMt5n6YzbHmoVtAnsvN+h37GNo7idmj+zJl+UZ/7Ui187TTrohI8xHxOiPRoHVGZN22Yq598V91Pv+psQO4fEB3wN+jMvfDbPJc7uDrWkNERKTx1fX5rb1ppEUoLHMf+6RqUhLig78e2S+di/umsX7HPgrL3NpfRkSkmVEYkRaherg4lnSnP2xUF2MzGNo7qaGbJSIiDaDR1xkRaQiDT+xCujM+bDHqkVQHIiLSsiiMSIsQYzOYPbovQK2BpFP7OBaPG6g6EBGRFkZhRFqMkf3SeW7cQNKcoUM2ndrHcefwk9lw/8UKIiIiLZBqRqRFUTGqiEjrozAiLY6KUUVEWhcN04iIiEhUKYyIiIhIVCmMiIiISFQpjIiIiEhUKYyIiIhIVCmMiIiISFQpjIiIiEhUKYyIiIhIVCmMiIiISFQpjIiIiEhUaTl4OW4+09JeMSIiUm8KI1JvPtNi4T9+4OUvdlJysDJ4PN0Zz+zRfbWDroiI1ImGaaResjbnMeihT3ny7z+EBBGAfJebKcs3krU5L0qtExGRlkRhRCKWtTmPKcs3UnKgMuzr1qH/zv0wG59phT1HREQkQMM0UieBupD8Ujfz/vYNx4oYFpDncrN+xz6G9k5qiiaKiEgLpTAix5S1OY+5H2aT53JHfG1hWeTXiIhI26IwIkcVGJKp72BLSkJ8g7ZHRERaH9WMSK18psXcD7PrHUTSnf5pviIiIkejMCK1Wr9jX72GZgJmj+6r9UZEROSYFEakVvWt9+jUPo7F4wZqnREREakT1YxIiOqrqRaVeSK6tlP7OCadcyLTLuqjHhEREakzhREJWvVVHve/v5l9Fd7gMZsBtS0VYgBdOti5/9LTSHO20zLwIiJSLwojAsD8Vdk8//mOGsePFkQAHv5VPw3HiIjIcVHNiLDqqx/DBpHqjuzwSHPG85zqQkREpAGoZ6SN85kW97+/+ZjnmRbMuvQ0khMc2plXREQalMJIG7d+xz72VYTfY+ZIyQkOLh/QvZFbJCIibY2Gadq4SKbvajVVERFpDAojbVxdA0ZSB7tWUxURkUahMNLGDT6xC+nOYweSeZf3U42IiIg0CoWRNi7GZjB7dF+OFjN+87MTGXWGZs2IiEjjUBgRRvZL57lxA2v0kHTpEMez153JzFF9o9QyERFpC+o1m2bRokU8+uij5Ofn079/f5555hkGDx4c9txly5YxadKkkGMOhwO3u/4bsEnDG9kvnYv7pgWXgtf0XRERaSoRh5G33nqL6dOns3jxYoYMGcKCBQsYMWIEW7ZsISUlJew1iYmJbNmyJfi9YegB1xzF2AyG9k6KdjNERKSNiXiY5oknnmDy5MlMmjSJvn37snjxYtq3b8/SpUtrvcYwDNLS0oJfqampx9VoERERaT0iCiNer5cNGzYwfPjwwz/AZmP48OGsW7eu1uvKy8vp0aMHmZmZXH755XzzzTdHfR+Px0NpaWnIl4iIiLROEYWRoqIifD5fjZ6N1NRU8vPzw15zyimnsHTpUt5//32WL1+OaZqcc8455Obm1vo+8+fPx+l0Br8yMzMjaaaIiIi0II0+m2bo0KGMHz+eAQMGcMEFF7By5Uq6du3K888/X+s1M2fOxOVyBb9ycnIau5kiIiISJREVsCYnJxMTE0NBQUHI8YKCAtLS0ur0M+Li4jjzzDPZunVrrec4HA4cDkckTRMREZEWKqKeEbvdzqBBg1i9enXwmGmarF69mqFDh9bpZ/h8Pr7++mvS07WIloiIiNRjau/06dOZMGECP/3pTxk8eDALFiygoqIiuJbI+PHj6d69O/PnzwfgwQcf5Oyzz6ZPnz6UlJTw6KOPsmvXLm6++eaG/SQiIiLSIkUcRsaMGcPevXt54IEHyM/PZ8CAAWRlZQWLWnfv3o3NdrjDZf/+/UyePJn8/Hw6d+7MoEGD+Oc//0nfvlrVU0RERMCwLMuKdiOOpbS0FKfTicvlIjExMdrNaXQ+09JKqCIi0uLV9fldr+XgpfFkbc5j7ofZ5LkOL5ef7oxn9ui+jOynOhsREWl9tFFeM5K1OY8pyzeGBBGAfJebKcs3krU5L0otExERaTwKI82Ez7SY+2E24cbMAsfmfpiNz2z2o2oiIiIRURhpJtbv2FejR6Q6C8hzuVm/Y1/TNUpERKQJKIw0E4VltQeR+pwnIiLSUiiMNBMpCfENep6IiEhLoTDSTAw+sQvpznhqm8Br4J9VM/jELk3ZLBERkUanMNJMxNgMZo/2LwR3ZCAJfD97dF+tNyIiIq2OwkgzMrJfOs+NG0iaM3QoJs0Zz3PjBmqdERERaZW06FkzM7JfOhf3TdMKrCIi0mYojDRDMTaDob2Tot0MERGRJqFhGhEREYkqhRERERGJKg3TNDDtuCsiIhIZhZEGpB13RUREIqdhmgaiHXdFRETqR2GkAWjHXRERkfpTGGkA2nFXRESk/hRGGoB23BUREak/hZEGoB13RURE6k9hpAFox10REZH6UxiJgM+0WLetmPc37WHdtuJgQap23BUREak/rTNSR8daQySw4+6R56RpnREREZGjMizLavbzTUtLS3E6nbhcLhITE5v8/QNriBx5owL9HM+NGxgMG1qBVURExK+uz2/1jBzDsdYQMfCvIXJx3zRibIZ23BUREYmQakaOQWuIiIiINC6FkWPQGiIiIiKNS2HkGLSGiIiISONqszUjdS00Dawhku9yh60bMfDPmNEaIiIiIvXTJsPIsabpVhdYQ2TK8o0YEBJItIaIiIjI8WtzwzSBabpHFqXmu9xMWb6RrM15Na4JrCGS5gwdiklzxodM6xUREZHItamekUin6VY3sl86F/dN0xoiIiIiDaxNhZFIpumGWytEa4iIiIg0vDY1TKNpuiIiIs1PmwojmqYrIiLS/LSpMBKYpltblYeBf1aNpumKiIg0nTYVRgLTdIEagUTTdEVERKKjTYUR0DRdERGR5qZNzaYJ0DRdERGR5qNePSOLFi2iZ8+exMfHM2TIENavX1+n61asWIFhGFxxxRX1edsGFZime/mA7gztnaQgIiIiEiURh5G33nqL6dOnM3v2bDZu3Ej//v0ZMWIEhYWFR71u586d/P73v+f888+vd2NFRESk9Yk4jDzxxBNMnjyZSZMm0bdvXxYvXkz79u1ZunRprdf4fD6uv/565s6dS69evY75Hh6Ph9LS0pAvERERaZ0iCiNer5cNGzYwfPjwwz/AZmP48OGsW7eu1usefPBBUlJSuOmmm+r0PvPnz8fpdAa/MjMzI2mmiIiItCARhZGioiJ8Ph+pqakhx1NTU8nPzw97zdq1a1myZAkvvvhind9n5syZuFyu4FdOTk4kzRQREZEWpFFn05SVlXHDDTfw4osvkpycXOfrHA4HDoejEVsmIiIizUVEYSQ5OZmYmBgKCgpCjhcUFJCWllbj/G3btrFz505Gjx4dPGaapv+NY2PZsmULvXv3rk+7RUREpJWIaJjGbrczaNAgVq9eHTxmmiarV69m6NChNc4/9dRT+frrr9m0aVPw67LLLmPYsGFs2rRJtSAiIiIS+TDN9OnTmTBhAj/96U8ZPHgwCxYsoKKigkmTJgEwfvx4unfvzvz584mPj6dfv34h13fq1AmgxnERERFpmyIOI2PGjGHv3r088MAD5OfnM2DAALKysoJFrbt378Zma3OrzIuIiEg9GZZlWdFuxLG4XC46depETk4OiYmJ0W6OiIiI1EFpaSmZmZmUlJTgdDprPa9F7E1TVlYGoBoTERGRFqisrOyoYaRF9IyYpsmPP/5IQkIChhF+D5lA+lLvie5FgO6Dn+6Dn+7DYboXfroPfo15HyzLoqysjG7duh21hKNF9IzYbDYyMjLqdG5iYmKb/kNVne6Fn+6Dn+6Dn+7DYboXfroPfo11H47WIxKgSlMRERGJKoURERERiapWE0YcDgezZ8/WMvLoXgToPvjpPvjpPhyme+Gn++DXHO5DiyhgFRERkdar1fSMiIiISMukMCIiIiJRpTAiIiIiUaUwIiIiIlHVosLIokWL6NmzJ/Hx8QwZMoT169fX6boVK1ZgGAZXXHFF4zawiURyH5YtW4ZhGCFf8fHxTdjaxhXpn4mSkhKmTp1Keno6DoeDk08+mVWrVjVRaxtPJPfhwgsvrPFnwjAMLr300iZsceOI9M/DggULOOWUU2jXrh2ZmZnceeeduN3uJmpt44rkXlRWVvLggw/Su3dv4uPj6d+/P1lZWU3Y2sbx+eefM3r0aLp164ZhGLz33nvHvGbNmjUMHDgQh8NBnz59WLZsWaO3s7FFeh/y8vK47rrrOPnkk7HZbNxxxx2N30irhVixYoVlt9utpUuXWt988401efJkq1OnTlZBQcFRr9uxY4fVvXt36/zzz7cuv/zypmlsI4r0Prz88stWYmKilZeXF/zKz89v4lY3jkjvhcfjsX76059ao0aNstauXWvt2LHDWrNmjbVp06YmbnnDivQ+FBcXh/x52Lx5sxUTE2O9/PLLTdvwBhbpfXj99dcth8Nhvf7669aOHTusjz/+2EpPT7fuvPPOJm55w4v0Xtx9991Wt27drI8++sjatm2b9eyzz1rx8fHWxo0bm7jlDWvVqlXWfffdZ61cudICrL/+9a9HPX/79u1W+/btrenTp1vZ2dnWM888Y8XExFhZWVlN0+BGEul92LFjh3XbbbdZr7zyijVgwADr9ttvb/Q2tpgwMnjwYGvq1KnB730+n9WtWzdr/vz5tV5TVVVlnXPOOdZLL71kTZgwoVWEkUjvw8svv2w5nc4mal3TivRePPfcc1avXr0sr9fbVE1sEvX5u1Hdk08+aSUkJFjl5eWN1cQmEel9mDp1qnXRRReFHJs+fbp17rnnNmo7m0Kk9yI9Pd1auHBhyLErr7zSuv766xu1nU2pLg/hu+++2/rJT34ScmzMmDHWiBEjGrFlTasu96G6Cy64oEnCSIsYpvF6vWzYsIHhw4cHj9lsNoYPH866detqve7BBx8kJSWFm266qSma2ejqex/Ky8vp0aMHmZmZXH755XzzzTdN0dxGVZ978cEHHzB06FCmTp1Kamoq/fr145FHHsHn8zVVsxtcff9MVLdkyRLGjh1Lhw4dGquZja4+9+Gcc85hw4YNweGL7du3s2rVKkaNGtUkbW4s9bkXHo+nxvBtu3btWLt2baO2tblZt25dyH0DGDFiRJ3/Lkn9tYgwUlRUhM/nIzU1NeR4amoq+fn5Ya9Zu3YtS5Ys4cUXX2yKJjaJ+tyHU045haVLl/L++++zfPlyTNPknHPOITc3tyma3Gjqcy+2b9/OO++8g8/nY9WqVcyaNYvHH3+chx56qCma3Cjqcx+qW79+PZs3b+bmm29urCY2ifrch+uuu44HH3yQ8847j7i4OHr37s2FF17Ivffe2xRNbjT1uRcjRozgiSee4IcffsA0TT799FNWrlxJXl5eUzS52cjPzw9730pLSzl48GCUWtU2tIgwEqmysjJuuOEGXnzxRZKTk6PdnKgaOnQo48ePZ8CAAVxwwQWsXLmSrl278vzzz0e7aU3ONE1SUlJ44YUXGDRoEGPGjOG+++5j8eLF0W5a1CxZsoTTTz+dwYMHR7spTW7NmjU88sgjPPvss2zcuJGVK1fy0UcfMW/evGg3rck99dRTnHTSSZx66qnY7XamTZvGpEmTjrrlu0hDio12A+oiOTmZmJgYCgoKQo4XFBSQlpZW4/xt27axc+dORo8eHTxmmiYAsbGxbNmyhd69ezduoxtBpPchnLi4OM4880y2bt3aGE1sMvW5F+np6cTFxRETExM8dtppp5Gfn4/X68VutzdqmxvD8fyZqKioYMWKFTz44ION2cQmUZ/7MGvWLG644YZgr9Dpp59ORUUFt9xyC/fdd1+LfRDX51507dqV9957D7fbTXFxMd26dWPGjBn06tWrKZrcbKSlpYW9b4mJibRr1y5KrWobWsTfNrvdzqBBg1i9enXwmGmarF69mqFDh9Y4/9RTT+Xrr79m06ZNwa/LLruMYcOGsWnTJjIzM5uy+Q0m0vsQjs/n4+uvvyY9Pb2xmtkk6nMvzj33XLZu3RoMpgDff/896enpLTKIwPH9mXj77bfxeDyMGzeusZvZ6OpzHw4cOFAjcASCqtWCt+w6nj8T8fHxdO/enaqqKt59910uv/zyxm5uszJ06NCQ+wbw6aef1vn/r3IcGr1EtoGsWLHCcjgc1rJly6zs7GzrlltusTp16hScpnrDDTdYM2bMqPX61jKbJtL7MHfuXOvjjz+2tm3bZm3YsMEaO3asFR8fb33zzTfR+ggNJtJ7sXv3bishIcGaNm2atWXLFutvf/ublZKSYj300EPR+ggNor5/N8477zxrzJgxTd3cRhPpfZg9e7aVkJBgvfnmm9b27dutTz75xOrdu7f161//OlofocFEei/+9a9/We+++661bds26/PPP7cuuugi68QTT7T2798fpU/QMMrKyqwvv/zS+vLLLy3AeuKJJ6wvv/zS2rVrl2VZljVjxgzrhhtuCJ4fmNr7hz/8wfr222+tRYsWtYqpvZHeB8uygucPGjTIuu6666wvv/yyUZ8bLSaMWJZlPfPMM9YJJ5xg2e12a/Dgwda//vWv4GsXXHCBNWHChFqvbS1hxLIiuw933HFH8NzU1FRr1KhRLX7tgOoi/TPxz3/+0xoyZIjlcDisXr16WQ8//LBVVVXVxK1ueJHeh++++84CrE8++aSJW9q4IrkPlZWV1pw5c6zevXtb8fHxVmZmpvW73/2uxT+AAyK5F2vWrLFOO+00y+FwWElJSdYNN9xg7dmzJwqtblifffaZBdT4Cnz2CRMmWBdccEGNawYMGGDZ7XarV69eLX79Hcuq330Id36PHj0arY3GoTcVERERiYoWUTMiIiIirZfCiIiIiESVwoiIiIhElcKIiIiIRJXCiIiIiESVwoiIiIhElcKIiIiIRJXCiIiIiESVwoiIiEgb9fnnnzN69Gi6deuGYRi89957Ef8My7J47LHHOPnkk3E4HHTv3p2HH344op/RInbtFRERkYZXUVFB//79ufHGG7nyyivr9TNuv/12PvnkEx577DFOP/109u3bx759+yL6GVoOXkRERDAMg7/+9a9cccUVwWMej4f77ruPN998k5KSEvr168ef/vQnLrzwQgC+/fZbzjjjDDZv3swpp5xS7/fWMI2IiIiENW3aNNatW8eKFSv46quvuOaaaxg5ciQ//PADAB9++CG9evXib3/7GyeeeCI9e/bk5ptvjrhnRGFEREREati9ezcvv/wyb7/9Nueffz69e/fm97//Peeddx4vv/wyANu3b2fXrl28/fbbvPrqqyxbtowNGzZw9dVXR/ReqhkRERGRGr7++mt8Ph8nn3xyyHGPx0NSUhIApmni8Xh49dVXg+ctWbKEQYMGsWXLljoP3SiMiIiISA3l5eXExMSwYcMGYmJiQl7r2LEjAOnp6cTGxoYEltNOOw3w96wojIiIiEi9nXnmmfh8PgoLCzn//PPDnnPuuedSVVXFtm3b6N27NwDff/89AD169Kjze2k2jYiISBtVXl7O1q1bAX/4eOKJJxg2bBhdunThhBNOYNy4cXzxxRc8/vjjnHnmmezdu5fVq1dzxhlncOmll2KaJmeddRYdO3ZkwYIFmKbJ1KlTSUxM5JNPPqlzOxRGRERE2qg1a9YwbNiwGscnTJjAsmXLqKys5KGHHuLVV19lz549JCcnc/bZZzN37lxOP/10AH788UduvfVWPvnkEzp06MAll1zC448/TpcuXercDoURERERiSpN7RUREZGoUhgRERGRqFIYERERkahSGBEREZGoUhgRERGRqFIYERERkahSGBEREZGoUhgRERGRqFIYERERkahSGBEREZGoUhgRERGRqPr/20b+5pbo40AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 1488\n",
      "reclassifying HD 7098\n",
      "reclassifying HD 11032\n",
      "reclassifying HD 13752\n",
      "There are 429 HDs still with both discontinuity problems\n",
      "Additionally, 922 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 1444\n",
      "working on enclave-generating HD 3763\n",
      "working on enclave-generating HD 5977\n",
      "working on enclave-generating HD 7063\n",
      "working on enclave-generating HD 7812\n",
      "working on enclave-generating HD 8631\n",
      "working on enclave-generating HD 10887\n",
      "working on enclave-generating HD 11592\n",
      "working on enclave-generating HD 12271\n",
      "working on enclave-generating HD 12957\n",
      "working on enclave-generating HD 13880\n",
      "working on enclave-generating HD 8480\n",
      "Out of 5923 HDpolys that generated enclaves, 0 had contiguous adjrs, while 4581 neighbored a state boundary while 1342 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 815494 district pop vs 776661 target\n",
      "working on enclave-y HD 2245 . We have evaluated 0 of 5923 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 6489 . We have evaluated 100 of 5923 enclavy HDs.Time is now 57\n",
      "working on enclave-y HD 7448 . We have evaluated 200 of 5923 enclavy HDs.Time is now 118\n",
      "working on enclave-y HD 7626 . We have evaluated 300 of 5923 enclavy HDs.Time is now 165\n",
      "working on enclave-y HD 7819 . We have evaluated 400 of 5923 enclavy HDs.Time is now 206\n",
      "working on enclave-y HD 8095 . We have evaluated 500 of 5923 enclavy HDs.Time is now 251\n",
      "working on enclave-y HD 8333 . We have evaluated 600 of 5923 enclavy HDs.Time is now 288\n",
      "working on enclave-y HD 11318 . We have evaluated 700 of 5923 enclavy HDs.Time is now 336\n",
      "working on enclave-y HD 11979 . We have evaluated 800 of 5923 enclavy HDs.Time is now 370\n",
      "working on enclave-y HD 12541 . We have evaluated 900 of 5923 enclavy HDs.Time is now 409\n",
      "working on enclave-y HD 13337 . We have evaluated 1000 of 5923 enclavy HDs.Time is now 442\n",
      "working on enclave-y HD 13766 . We have evaluated 1100 of 5923 enclavy HDs.Time is now 491\n",
      "working on enclave-y HD 7446 . We have evaluated 1200 of 5923 enclavy HDs.Time is now 543\n",
      "working on enclave-y HD 12348 . We have evaluated 1300 of 5923 enclavy HDs.Time is now 595\n",
      "working on enclave-y HD 1553 . We have evaluated 1400 of 5923 enclavy HDs.Time is now 632\n",
      "working on enclave-y HD 2283 . We have evaluated 1500 of 5923 enclavy HDs.Time is now 665\n",
      "working on enclave-y HD 2815 . We have evaluated 1600 of 5923 enclavy HDs.Time is now 703\n",
      "working on enclave-y HD 3510 . We have evaluated 1700 of 5923 enclavy HDs.Time is now 738\n",
      "working on enclave-y HD 3693 . We have evaluated 1800 of 5923 enclavy HDs.Time is now 765\n",
      "working on enclave-y HD 4053 . We have evaluated 1900 of 5923 enclavy HDs.Time is now 796\n",
      "working on enclave-y HD 4217 . We have evaluated 2000 of 5923 enclavy HDs.Time is now 835\n",
      "working on enclave-y HD 5634 . We have evaluated 2100 of 5923 enclavy HDs.Time is now 870\n",
      "working on enclave-y HD 5794 . We have evaluated 2200 of 5923 enclavy HDs.Time is now 902\n",
      "working on enclave-y HD 5985 . We have evaluated 2300 of 5923 enclavy HDs.Time is now 926\n",
      "working on enclave-y HD 6196 . We have evaluated 2400 of 5923 enclavy HDs.Time is now 960\n",
      "working on enclave-y HD 6425 . We have evaluated 2500 of 5923 enclavy HDs.Time is now 998\n",
      "working on enclave-y HD 6803 . We have evaluated 2600 of 5923 enclavy HDs.Time is now 1034\n",
      "working on enclave-y HD 7056 . We have evaluated 2700 of 5923 enclavy HDs.Time is now 1070\n",
      "working on enclave-y HD 7253 . We have evaluated 2800 of 5923 enclavy HDs.Time is now 1119\n",
      "working on enclave-y HD 7632 . We have evaluated 2900 of 5923 enclavy HDs.Time is now 1157\n",
      "working on enclave-y HD 7977 . We have evaluated 3000 of 5923 enclavy HDs.Time is now 1194\n",
      "working on enclave-y HD 8306 . We have evaluated 3100 of 5923 enclavy HDs.Time is now 1237\n",
      "working on enclave-y HD 10171 . We have evaluated 3200 of 5923 enclavy HDs.Time is now 1271\n",
      "working on enclave-y HD 10331 . We have evaluated 3300 of 5923 enclavy HDs.Time is now 1307\n",
      "working on enclave-y HD 10483 . We have evaluated 3400 of 5923 enclavy HDs.Time is now 1346\n",
      "working on enclave-y HD 10643 . We have evaluated 3500 of 5923 enclavy HDs.Time is now 1376\n",
      "working on enclave-y HD 10808 . We have evaluated 3600 of 5923 enclavy HDs.Time is now 1409\n",
      "working on enclave-y HD 10966 . We have evaluated 3700 of 5923 enclavy HDs.Time is now 1447\n",
      "working on enclave-y HD 11115 . We have evaluated 3800 of 5923 enclavy HDs.Time is now 1482\n",
      "working on enclave-y HD 11280 . We have evaluated 3900 of 5923 enclavy HDs.Time is now 1516\n",
      "working on enclave-y HD 11449 . We have evaluated 4000 of 5923 enclavy HDs.Time is now 1541\n",
      "working on enclave-y HD 11597 . We have evaluated 4100 of 5923 enclavy HDs.Time is now 1565\n",
      "working on enclave-y HD 11778 . We have evaluated 4200 of 5923 enclavy HDs.Time is now 1595\n",
      "working on enclave-y HD 11938 . We have evaluated 4300 of 5923 enclavy HDs.Time is now 1624\n",
      "working on enclave-y HD 12135 . We have evaluated 4400 of 5923 enclavy HDs.Time is now 1647\n",
      "working on enclave-y HD 12322 . We have evaluated 4500 of 5923 enclavy HDs.Time is now 1672\n",
      "working on enclave-y HD 12488 . We have evaluated 4600 of 5923 enclavy HDs.Time is now 1699\n",
      "working on enclave-y HD 12693 . We have evaluated 4700 of 5923 enclavy HDs.Time is now 1726\n",
      "working on enclave-y HD 12858 . We have evaluated 4800 of 5923 enclavy HDs.Time is now 1747\n",
      "working on enclave-y HD 13038 . We have evaluated 4900 of 5923 enclavy HDs.Time is now 1775\n",
      "working on enclave-y HD 13430 . We have evaluated 5000 of 5923 enclavy HDs.Time is now 1817\n",
      "working on enclave-y HD 13585 . We have evaluated 5100 of 5923 enclavy HDs.Time is now 1858\n",
      "working on enclave-y HD 1828 . We have evaluated 5200 of 5923 enclavy HDs.Time is now 1903\n",
      "working on enclave-y HD 5661 . We have evaluated 5300 of 5923 enclavy HDs.Time is now 1945\n",
      "working on enclave-y HD 6590 . We have evaluated 5400 of 5923 enclavy HDs.Time is now 1988\n",
      "working on enclave-y HD 7821 . We have evaluated 5500 of 5923 enclavy HDs.Time is now 2036\n",
      "working on enclave-y HD 10371 . We have evaluated 5600 of 5923 enclavy HDs.Time is now 2082\n",
      "working on enclave-y HD 11181 . We have evaluated 5700 of 5923 enclavy HDs.Time is now 2127\n",
      "working on enclave-y HD 12188 . We have evaluated 5800 of 5923 enclavy HDs.Time is now 2153\n",
      "working on enclave-y HD 13532 . We have evaluated 5900 of 5923 enclavy HDs.Time is now 2181\n",
      "Out of 5923 HDs with enclaves 5923 had enclaves.\n",
      "Of these, 5707 wouldn't be over 815494 if all enclaves filled, while 216 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1342 4581 1207 749\n",
      "216 216 5707\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))\n",
    "#610 3379 2187 1954\n",
    "#80 80 3909  for original MN option3 (assigned CCBs by captured area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00394 0.07738\n",
      "CCB cluster 0 = unit 7192 with pop 495747.0 now has use of 1.0007636957069508\n",
      "CCB cluster 1 = unit 7193 with pop 52630.0 now has use of 1.0000465238927125\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 216 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 1444\n",
      "working on avg unit-to-HDcp dist for HD 3788\n",
      "working on avg unit-to-HDcp dist for HD 6027\n",
      "working on avg unit-to-HDcp dist for HD 6911\n",
      "working on avg unit-to-HDcp dist for HD 7832\n",
      "working on avg unit-to-HDcp dist for HD 10265\n",
      "working on avg unit-to-HDcp dist for HD 11093\n",
      "working on avg unit-to-HDcp dist for HD 11913\n",
      "working on avg unit-to-HDcp dist for HD 12734\n",
      "working on avg unit-to-HDcp dist for HD 13819\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([7192, 7193] )  #lock in the clusters  #update this for each state - see CCB report 3 blocks up\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 216 HDs\n",
      "working on HD 1460 cantFill 0 of 216 .Time is now 0\n",
      "working on HD 2808 cantFill 20 of 216 .Time is now 0\n",
      "working on HD 3625 cantFill 40 of 216 .Time is now 1\n",
      "working on HD 4066 cantFill 60 of 216 .Time is now 2\n",
      "working on HD 5753 cantFill 80 of 216 .Time is now 3\n",
      "working on HD 5914 cantFill 100 of 216 .Time is now 3\n",
      "working on HD 6744 cantFill 120 of 216 .Time is now 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wise.gm\\AppData\\Local\\Temp\\ipykernel_1172\\2612373365.py:64: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 11412 cantFill 140 of 216 .Time is now 4\n",
      "working on HD 11998 cantFill 160 of 216 .Time is now 5\n",
      "working on HD 13343 cantFill 180 of 216 .Time is now 5\n",
      "working on HD 5748 cantFill 200 of 216 .Time is now 6\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00392 0.07736\n",
      "CCB cluster 0 = unit 7192 with pop 495747.0 now has use of 1.0007636957069508\n",
      "CCB cluster 1 = unit 7193 with pop 52630.0 now has use of 1.0000465238927125\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 1500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7500\n",
      "working on HD 8000\n",
      "working on HD 8500\n",
      "working on HD 10500\n",
      "working on HD 11000\n",
      "working on HD 11500\n",
      "working on HD 12000\n",
      "working on HD 12500\n",
      "working on HD 13000\n",
      "out of 7263 total HDs, there were 7044 26 13 180 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code  #Note for NYlower -- ran this twice as CANSWELL got hung up.  originally ~462 to fix\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 1444\n",
      "working on current HD diam for HD number 3788\n",
      "working on current HD diam for HD number 6027\n",
      "working on current HD diam for HD number 6911\n",
      "working on current HD diam for HD number 7832\n",
      "working on current HD diam for HD number 10265\n",
      "working on current HD diam for HD number 11093\n",
      "working on current HD diam for HD number 11913\n",
      "working on current HD diam for HD number 12734\n",
      "working on current HD diam for HD number 13819\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38833 but less than 194165\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 206 HDs to triage in this block\n",
      "working on swell-filling discontig HD 1549 our 1 th HD we think we can swell out of 206 0.001 sec elapsed\n",
      "working on swell-filling discontig HD 11190 our 41 th HD we think we can swell out of 206 844.372 sec elapsed\n",
      "working on swell-filling discontig HD 11803 our 81 th HD we think we can swell out of 206 3783.656 sec elapsed\n",
      "working on swell-filling discontig HD 12235 our 121 th HD we think we can swell out of 206 4308.927 sec elapsed\n",
      "working on swell-filling discontig HD 12565 our 161 th HD we think we can swell out of 206 5614.258 sec elapsed\n",
      "working on swell-filling discontig HD 13044 our 201 th HD we think we can swell out of 206 6190.131 sec elapsed\n",
      "we partially regrew 169 HDs, leaving 50 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE  #run twice.  first time did about 250 \n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00392 0.07736\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.9997 0.07361\n",
      "CCB cluster 0 = unit 7192 with pop 495747.0 now has use of 0.9438354468800039\n",
      "CCB cluster 1 = unit 7193 with pop 52630.0 now has use of 0.9988812806397068\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 1444 time is now 0\n",
      "working on HD 4146 time is now 19\n",
      "working on HD 6482 time is now 35\n",
      "working on HD 7625 time is now 50\n",
      "working on HD 10265 time is now 68\n",
      "working on HD 11301 time is now 86\n",
      "working on HD 12320 time is now 101\n",
      "working on HD 13604 time is now 118\n",
      "out of 7263 total HDs, there were 7226.0 contiguous and 7215.0 complement-contiguous HDs\n",
      "11 HDs had both discontiguity problems, while enclave-only = 37 and discontig only= 26\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 37 37\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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O5dVXX9W+ffuc2vbv39/xc3R0tCTpyJEjkqTc3FyNGDGi1v2uWrVKI0aMUPv27RUcHKzf/e53OnbsmOMjleuuu06+vr569913JUn/+c9/FBISosTEREnS9u3btXfvXgUHBztqDg8P18mTJ8+p2xUSEhLOWT975mPPnj0aMGCAWrVq5dg/bNgwVVdXO300NWDAAAUGBjodo7S0VAUFBS6v10pccAoAqJOz1yUsW7ZM7du3d9pnt9ud1n19fR0/22w2SWeuF5GkgICAWvf51Vdf6Re/+IXuvPNOPfHEEwoPD9fHH3+sCRMmqLKyUoGBgfLz89OvfvUrLVy4UGPGjNHChQs1evRo+fj4OOoeNGiQXn/99XOO365du1rXgobjzAcAoE569+4tu92uAwcOqGvXrk5LbGzt75bp37+/srOza9V269atqq6u1tNPP63LL79c3bt317fffntOu5SUFC1fvlyff/65Vq9erZSUFMe+yy67TF9++aUiIiLOqTs0NLTWddfWpk2bzlnv1auXJKlXr17avn27ysrKHPs3bNggLy8v9ejRw7Ft+/btOnHihNMxgoKC6vR7bo4IHwCAOgkODtb999+vqVOnasGCBdq3b58+++wzvfDCC1qwYEGtj5Oenq5FixYpPT1de/bs0c6dO/Xkk0/W2LZr166qqqrSCy+8oP379+u1117TSy+9dE674cOHKyoqSikpKYqLi1N8fLxjX0pKitq2basbb7xRH330kfLz87V27Vrdc889+uabb85b5969e5Wbm6vCwkKdOHFCubm5ys3NvehFqosXL9a//vUvffHFF0pPT9fmzZsdF5SmpKTI399f48aN065du7RmzRrdfffd+t3vfqfIyEjHMSorKzVhwgTt3r1bH3zwgdLT0zV58mR5ebn3n28+dgGA5uq7L5ptP48//rjatWunjIwM7d+/X2FhYbrsssv0pz/9qdbHuPrqq7V48WI9/vjjmjVrlkJCQjR8+PAa2w4YMEDPPPOMnnzySaWlpWn48OHKyMjQ73//e6d2NptNY8eO1ezZszVjxgynfYGBgVq/fr2mT5+um2++WcePH1f79u01YsQIhYSEnLfO22+/3ekunoEDB0qS8vPz1blz5/M+bubMmcrKytJdd92l6OhoLVq0SL1793bUsmLFCt17770aMmSIAgMDNWrUKD3zzDNOxxgxYoS6deum4cOHq6KiQmPHjtWjjz563j7dhc0YY5q6iB8rKSlRaGioiouLL/hiqJdvc6V/XCXdse7Mve1Waap+ATR7J0+eVH5+vuLi4uTv739mIzOcuj2bzaYlS5Zo5MiR9T7G+PHjVVRUpKVLl7qsroaq8fX6f+ry97tOZz4yMzOVmZnpmJClT58+mjFjhpKTkx1F3XfffcrKylJFRYWSkpL04osvOp1CAgBcRFjsmSDQwr/bBZ6rTuGjQ4cOmjVrlrp16yZjjBYsWKAbb7xR27ZtU58+fTR16lQtW7ZMixcvVmhoqCZPnqybb75ZGzZsaKz6AcAzhcUSBuCx6hQ+brjhBqf1J554QpmZmdq0aZM6dOigV155RQsXLtTPfvYzSdK8efPUq1cvbdq0SZdffrnrqgYAoBlzxRUN8+fPb3ghzVS9L5c9ffq0srKyVFZWpoSEBG3dulVVVVWOyVwkqWfPnurYsaM2btx43uNUVFSopKTEaQEAAJ6rzuFj586dCgoKkt1u16RJk7RkyRL17t1bhYWF8vPzU1hYmFP7yMhIFRYWnvd4GRkZCg0NdSzufu8yAAC4sDqHjx49eig3N1c5OTm68847NW7cOO3evbveBaSlpam4uNixuPuUsQBQH83sxkOgRq56ndZ5ng8/Pz917dpVkjRo0CB9+umnev755zV69GhVVlaqqKjI6ezH4cOHFRUVdd7j2e32c6bjBYCW4uz04+Xl5XWabhxoCmcnVvP29m7QcRo8yVh1dbUqKio0aNAg+fr6Kjs7W6NGjZIk5eXl6cCBA+d8uQ4A4Axvb2+FhYU5vmwtMDDQ8R0oQHNSXV2to0ePKjAw0PF9OfVVp0enpaUpOTlZHTt21PHjx7Vw4UKtXbtWK1asUGhoqCZMmKBp06YpPDxcISEhuvvuu5WQkMCdLgBwAWfPDp8NIEBz5eXlpY4dOzY4INcpfBw5ckS///3vdejQIYWGhqp///5asWKFfv7zn0uSnn32WXl5eWnUqFFOk4wBAM7PZrMpOjpaERERqqqqaupygPPy8/NzyffK1Cl8vPLKKxfc7+/vr7lz52ru3LkNKgoAWiJvb+8Gf5YOuAP3/lo8AADgdggfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFJ1Ch8ZGRkaMmSIgoODFRERoZEjRyovL8+pzdVXXy2bzea0TJo0yaVFAwAA91Wn8LFu3TqlpqZq06ZNWrlypaqqqnTNNdeorKzMqd3EiRN16NAhxzJ79myXFg0AANyXT10aL1++3Gl9/vz5ioiI0NatWzV8+HDH9sDAQEVFRbmmQgAA4FEadM1HcXGxJCk8PNxp++uvv662bduqb9++SktLU3l5eUO6AQAAHqROZz5+rLq6WlOmTNGwYcPUt29fx/bf/OY36tSpk2JiYrRjxw5Nnz5deXl5evvtt2s8TkVFhSoqKhzrJSUl9S0JAAC4gXqHj9TUVO3atUsff/yx0/Y77rjD8XO/fv0UHR2tESNGaN++fbrkkkvOOU5GRoZmzpxZ3zIAAICbqdfHLpMnT9b777+vNWvWqEOHDhdsGx8fL0nau3dvjfvT0tJUXFzsWAoKCupTEgAAcBN1OvNhjNHdd9+tJUuWaO3atYqLi7voY3JzcyVJ0dHRNe632+2y2+11KQMAALixOoWP1NRULVy4UO+8846Cg4NVWFgoSQoNDVVAQID27dunhQsX6rrrrlObNm20Y8cOTZ06VcOHD1f//v0bZQAAAMC91Cl8ZGZmSjozkdiPzZs3T+PHj5efn59WrVql5557TmVlZYqNjdWoUaP08MMPu6xgAADg3ur8scuFxMbGat26dQ0qCAAAeDa+2wUAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWqlP4yMjI0JAhQxQcHKyIiAiNHDlSeXl5Tm1Onjyp1NRUtWnTRkFBQRo1apQOHz7s0qIBAID7qlP4WLdunVJTU7Vp0yatXLlSVVVVuuaaa1RWVuZoM3XqVL333ntavHix1q1bp2+//VY333yzywsHAADuyacujZcvX+60Pn/+fEVERGjr1q0aPny4iouL9corr2jhwoX62c9+JkmaN2+eevXqpU2bNunyyy93XeUAAMAtNeiaj+LiYklSeHi4JGnr1q2qqqpSYmKio03Pnj3VsWNHbdy4scZjVFRUqKSkxGkBAACeq97ho7q6WlOmTNGwYcPUt29fSVJhYaH8/PwUFhbm1DYyMlKFhYU1HicjI0OhoaGOJTY2tr4lAQAAN1Dv8JGamqpdu3YpKyurQQWkpaWpuLjYsRQUFDToeAAAoHmr0zUfZ02ePFnvv/++1q9frw4dOji2R0VFqbKyUkVFRU5nPw4fPqyoqKgaj2W322W32+tTBgAAcEN1OvNhjNHkyZO1ZMkSrV69WnFxcU77Bw0aJF9fX2VnZzu25eXl6cCBA0pISHBNxQAAwK3V6cxHamqqFi5cqHfeeUfBwcGO6zhCQ0MVEBCg0NBQTZgwQdOmTVN4eLhCQkJ09913KyEhgTtdAACApDqGj8zMTEnS1Vdf7bR93rx5Gj9+vCTp2WeflZeXl0aNGqWKigolJSXpxRdfdEmxAADA/dUpfBhjLtrG399fc+fO1dy5c+tdFAAA8Fx8twsAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYqs7hY/369brhhhsUExMjm82mpUuXOu0fP368bDab03Lttde6ql4AAODm6hw+ysrKNGDAAM2dO/e8ba699lodOnTIsSxatKhBRQIAAM/hU9cHJCcnKzk5+YJt7Ha7oqKi6l0UAADwXI1yzcfatWsVERGhHj166M4779SxY8fO27aiokIlJSVOCwAA8FwuDx/XXnutXn31VWVnZ+vJJ5/UunXrlJycrNOnT9fYPiMjQ6GhoY4lNjbW1SUBAIBmpM4fu1zMmDFjHD/369dP/fv31yWXXKK1a9dqxIgR57RPS0vTtGnTHOslJSUEEAAAPFij32rbpUsXtW3bVnv37q1xv91uV0hIiNMCAAA8V6OHj2+++UbHjh1TdHR0Y3cFAADcQJ0/diktLXU6i5Gfn6/c3FyFh4crPDxcM2fO1KhRoxQVFaV9+/bpwQcfVNeuXZWUlOTSwgEAgHuqc/jYsmWLfvrTnzrWz16vMW7cOGVmZmrHjh1asGCBioqKFBMTo2uuuUaPP/647Ha766oGAABuq87h4+qrr5Yx5rz7V6xY0aCCAACAZ+O7XQAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBSdQ4f69ev1w033KCYmBjZbDYtXbrUab8xRjNmzFB0dLQCAgKUmJioL7/80lX1AgAAN1fn8FFWVqYBAwZo7ty5Ne6fPXu25syZo5deekk5OTlq1aqVkpKSdPLkyQYXCwAA3J9PXR+QnJys5OTkGvcZY/Tcc8/p4Ycf1o033ihJevXVVxUZGamlS5dqzJgxDasWAAC4PZde85Gfn6/CwkIlJiY6toWGhio+Pl4bN250ZVcAAMBN1fnMx4UUFhZKkiIjI522R0ZGOvb9r4qKClVUVDjWS0pKXFkSAABoZpr8bpeMjAyFhoY6ltjY2KYuCQAANCKXho+oqChJ0uHDh522Hz582LHvf6Wlpam4uNixFBQUuLIkAADQzLg0fMTFxSkqKkrZ2dmObSUlJcrJyVFCQkKNj7Hb7QoJCXFaAACA56rzNR+lpaXau3evYz0/P1+5ubkKDw9Xx44dNWXKFP35z39Wt27dFBcXp0ceeUQxMTEaOXKkK+sGAABuqs7hY8uWLfrpT3/qWJ82bZokady4cZo/f74efPBBlZWV6Y477lBRUZGuuOIKLV++XP7+/q6rGgAAuK06h4+rr75axpjz7rfZbHrsscf02GOPNagwAADgmZr8bhcAANCyED4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWMqnqQsAmruDRSf0Q1llU5dRa61b+al9WEBTlwEA50X4AC7gYNEJJT69TieqTjd1KbUW4OutVfddRQAB0GwRPoAL+KGsUieqTuu50Zeqa0RQU5dzUXuPlGrKG7n6oayS8AGg2SJ8ALXQNSJIfduHNnUZAOARuOAUAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGAp5vlwc0z9DQBwN4QPN8bU3wAAd0T4cGNM/Q0AcEeEDw/A1N8AAHfCBacAAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJbiVlvAA+09UtrUJdQas97C3bnbTNNS0/+7I3wAHqR1Kz8F+Hpryhu5TV1KrTHrLdyZO840LTX9vzvCB+BB2ocFaNV9V7nN/4Ux6y3cnbvNNC01j393hA/Aw7QPC+APOWAxZpquGy44BQAAliJ8AAAASxE+AACApQgfAADAUi4PH48++qhsNpvT0rNnT1d3AwAA3FSj3O3Sp08frVq16v934sNNNQAA4IxGSQU+Pj6KiopqjEMDAAA31yjXfHz55ZeKiYlRly5dlJKSogMHDpy3bUVFhUpKSpwWAADguVwePuLj4zV//nwtX75cmZmZys/P15VXXqnjx4/X2D4jI0OhoaGOJTY21tUlAQCAZsTl4SM5OVm33HKL+vfvr6SkJH3wwQcqKirSm2++WWP7tLQ0FRcXO5aCggJXlwQAAJqRRr8SNCwsTN27d9fevXtr3G+322W32xu7DAAA0Ew0+jwfpaWl2rdvn6Kjoxu7KwAA4AZcHj7uv/9+rVu3Tl999ZU++eQT3XTTTfL29tbYsWNd3RUAAHBDLv/Y5ZtvvtHYsWN17NgxtWvXTldccYU2bdqkdu3auborAADghlwePrKyslx9SAAA4EH4bhcAAGApwgcAALAU4QMAAFiKb3wDADQrB4tO6IeyyqYuo1b2Hilt6hLcEuEDANBsHCw6ocSn1+lE1emmLqXWAny91bqVX1OX4VYIHwCAZuOHskqdqDqt50Zfqq4RQU1dTq20buWn9mEBTV2GWyF8AACana4RQerbPrSpy0Aj4YJTAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLcavt/2iMmfX8vytVV0l7j5bqpCl22XGZWQ9oQkUFUvkx6/sNbCOFxVrfL+BChI8faayZ9frY8rXMLt2blavPXRg+JGbWA5pEUYE0d6hUVW59376BUupmAgjcGuHjRxprZj3/70KlJdLzYy7Vybb9XHZciZn1gCZRfuxM8Lj5Zaltd+v6/e4L6e2JZ/onfMCNET5q4PKZ9WxngkzXdkFSDDP2AR6jbXcp5tKmrgJwO1xwCgAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFPN8wHLuNC28O9UKAO6C8AHLtG7lpwBfb015I7epS6kTprAHANcifMAy7cMCtOq+q1z+xX2NjSnsAcC1CB+wVPuwAP6QA0ALxwWnAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLMc8HGk9RgVR+rGn6DmwjhcU2Td+oM3ebxj6irEIRTV0E4MYIH2gcRQXS3KFSVXnT9O8bKKVuJoA0c+465f4g36/1H++mrgJwX4QPNI7yY2eCx80vS227W9v3d19Ib088UwPho1lzxyn39x4p1ctv5kuED6DeCB9oXG27SzGXNnUVaMaYch9oebjgFAAAWIrwAQAALEX4AAAAlmq08DF37lx17txZ/v7+io+P1+bNmxurKwAA4EYaJXy88cYbmjZtmtLT0/XZZ59pwIABSkpK0pEjRxqjOwAA4EYaJXw888wzmjhxom699Vb17t1bL730kgIDA/Wvf/2rMboDAABuxOW32lZWVmrr1q1KS0tzbPPy8lJiYqI2btx4TvuKigpVVFQ41ouLiyVJJSUlri5NOl4qVZgz/63h+KXHS1RdUa7S4yUqKbG5vt+vcs/83BIc23vB33Wjaom/b1im6rsyxVbuV4mM9uUfVkVJgWV9248d1iUV1vdrpf1HyxrnfRgOjfW37uzfbWPMxRsbFzt48KCRZD755BOn7Q888IAZOnToOe3T09ONJBYWFhYWFhYPWAoKCi6aFZp8krG0tDRNmzbNsV5dXa3vv/9ebdq0kc3m2tRbUlKi2NhYFRQUKCQkxKXHbq5a4pglxt2Sxt0Sxyy1zHG3xDFL7jNuY4yOHz+umJiYi7Z1efho27atvL29dfjwYafthw8fVlRU1Dnt7Xa77Ha707awsDBXl+UkJCSkWT+BjaEljlli3C1JSxyz1DLH3RLHLLnHuENDQ2vVzuUXnPr5+WnQoEHKzs52bKuurlZ2drYSEhJc3R0AAHAzjfKxy7Rp0zRu3DgNHjxYQ4cO1XPPPaeysjLdeuutjdEdAABwI40SPkaPHq2jR49qxowZKiws1KWXXqrly5crMjKyMbqrNbvdrvT09HM+5vFkLXHMEuNuSeNuiWOWWua4W+KYJc8ct82Y2twTAwAA4Bp8twsAALAU4QMAAFiK8AEAACxF+AAAAJZqMeFj7ty56ty5s/z9/RUfH6/Nmzc3dUkO69ev1w033KCYmBjZbDYtXbrUab8xRjNmzFB0dLQCAgKUmJioL7/80qnN999/r5SUFIWEhCgsLEwTJkxQaanz95rs2LFDV155pfz9/RUbG6vZs2efU8vixYvVs2dP+fv7q1+/fvrggw/qXEttZGRkaMiQIQoODlZERIRGjhypvLw8pzYnT55Uamqq2rRpo6CgII0aNeqcyesOHDig66+/XoGBgYqIiNADDzygU6dOObVZu3atLrvsMtntdnXt2lXz588/p56LvT5qU8vFZGZmqn///o6JghISEvThhx967HjPZ9asWbLZbJoyZYpHj/3RRx+VzWZzWnr27OnRY5akgwcP6re//a3atGmjgIAA9evXT1u2bHHs98T3s86dO5/zXNtsNqWmpkry3Oe6QRr+bS7NX1ZWlvHz8zP/+te/zOeff24mTpxowsLCzOHDh5u6NGOMMR988IF56KGHzNtvv20kmSVLljjtnzVrlgkNDTVLly4127dvN7/85S9NXFycOXHihKPNtddeawYMGGA2bdpkPvroI9O1a1czduxYx/7i4mITGRlpUlJSzK5du8yiRYtMQECA+fvf/+5os2HDBuPt7W1mz55tdu/ebR5++GHj6+trdu7cWadaaiMpKcnMmzfP7Nq1y+Tm5prrrrvOdOzY0ZSWljraTJo0ycTGxprs7GyzZcsWc/nll5uf/OQnjv2nTp0yffv2NYmJiWbbtm3mgw8+MG3btjVpaWmONvv37zeBgYFm2rRpZvfu3eaFF14w3t7eZvny5Y42tXl9XKyW2nj33XfNsmXLzBdffGHy8vLMn/70J+Pr62t27drlkeOtyebNm03nzp1N//79zb333lvr/txx7Onp6aZPnz7m0KFDjuXo0aMePebvv//edOrUyYwfP97k5OSY/fv3mxUrVpi9e/c62nji+9mRI0ecnueVK1caSWbNmjXGGM98rhuqRYSPoUOHmtTUVMf66dOnTUxMjMnIyGjCqmr2v+GjurraREVFmb/+9a+ObUVFRcZut5tFixYZY4zZvXu3kWQ+/fRTR5sPP/zQ2Gw2c/DgQWOMMS+++KJp3bq1qaiocLSZPn266dGjh2P917/+tbn++uud6omPjzd/+MMfal1LfR05csRIMuvWrXMc19fX1yxevNjRZs+ePUaS2bhxozHmTGjz8vIyhYWFjjaZmZkmJCTEMc4HH3zQ9OnTx6mv0aNHm6SkJMf6xV4ftamlvlq3bm3++c9/tojxHj9+3HTr1s2sXLnSXHXVVY7w4aljT09PNwMGDKhxn6eOefr06eaKK6447/6W8n527733mksuucRUV1d77HPdUB7/sUtlZaW2bt2qxMRExzYvLy8lJiZq48aNTVhZ7eTn56uwsNCp/tDQUMXHxzvq37hxo8LCwjR48GBHm8TERHl5eSknJ8fRZvjw4fLz83O0SUpKUl5enn744QdHmx/3c7bN2X5qU0t9FRcXS5LCw8MlSVu3blVVVZVTXz179lTHjh2dxt2vXz+nyeuSkpJUUlKizz//vFZjqs3roza11NXp06eVlZWlsrIyJSQkePx4JSk1NVXXX3/9OfV58ti//PJLxcTEqEuXLkpJSdGBAwc8eszvvvuuBg8erFtuuUUREREaOHCgXn75Zcf+lvB+VllZqX//+9+67bbbZLPZPPa5biiPDx/fffedTp8+fc7sqpGRkSosLGyiqmrvbI0Xqr+wsFARERFO+318fBQeHu7UpqZj/LiP87X58f6L1VIf1dXVmjJlioYNG6a+ffs6+vLz8zvnSwb/t576jqmkpEQnTpyo1eujNrXU1s6dOxUUFCS73a5JkyZpyZIl6t27t8eO96ysrCx99tlnysjIOGefp449Pj5e8+fP1/Lly5WZman8/HxdeeWVOn78uMeOef/+/crMzFS3bt20YsUK3Xnnnbrnnnu0YMECp7o9+f1s6dKlKioq0vjx4x39eOJz3VCNMr06UBepqanatWuXPv7446YupdH16NFDubm5Ki4u1ltvvaVx48Zp3bp1TV1WoyooKNC9996rlStXyt/fv6nLsUxycrLj5/79+ys+Pl6dOnXSm2++qYCAgCasrPFUV1dr8ODB+stf/iJJGjhwoHbt2qWXXnpJ48aNa+LqrPHKK68oOTm5Vl8r35J5/JmPtm3bytvb+5yreQ8fPqyoqKgmqqr2ztZ4ofqjoqJ05MgRp/2nTp3S999/79SmpmP8uI/ztfnx/ovVUleTJ0/W+++/rzVr1qhDhw6O7VFRUaqsrFRRUdEF66nvmEJCQhQQEFCr10dtaqktPz8/de3aVYMGDVJGRoYGDBig559/3mPHK5051XvkyBFddtll8vHxkY+Pj9atW6c5c+bIx8dHkZGRHjv2HwsLC1P37t21d+9ej32+o6Oj1bt3b6dtvXr1cnzc5OnvZ19//bVWrVql22+/3bHNU5/rhvL48OHn56dBgwYpOzvbsa26ulrZ2dlKSEhowspqJy4uTlFRUU71l5SUKCcnx1F/QkKCioqKtHXrVkeb1atXq7q6WvHx8Y4269evV1VVlaPNypUr1aNHD7Vu3drR5sf9nG1ztp/a1FJbxhhNnjxZS5Ys0erVqxUXF+e0f9CgQfL19XXqKy8vTwcOHHAa986dO53eqFauXKmQkBDHG+DFxlSb10dtaqmv6upqVVRUePR4R4wYoZ07dyo3N9exDB48WCkpKY6fPXXsP1ZaWqp9+/YpOjraY5/vYcOGnXPL/BdffKFOnTpJ8tz3s7PmzZuniIgIXX/99Y5tnvpcN5ill7c2kaysLGO32838+fPN7t27zR133GHCwsKcrixuSsePHzfbtm0z27ZtM5LMM888Y7Zt22a+/vprY8yZ28HCwsLMO++8Y3bs2GFuvPHGGm9NGzhwoMnJyTEff/yx6datm9OtaUVFRSYyMtL87ne/M7t27TJZWVkmMDDwnFvTfHx8zFNPPWX27Nlj0tPTa7w17WK11Madd95pQkNDzdq1a51uUSsvL3e0mTRpkunYsaNZvXq12bJli0lISDAJCQmO/WdvT7vmmmtMbm6uWb58uWnXrl2Nt6c98MADZs+ePWbu3Lk13p52sdfHxWqpjT/+8Y9m3bp1Jj8/3+zYscP88Y9/NDabzfz3v//1yPFeyI/vdvHUsd93331m7dq1Jj8/32zYsMEkJiaatm3bmiNHjnjsmDdv3mx8fHzME088Yb788kvz+uuvm8DAQPPvf//b0cYT38+MOXNnSceOHc306dPP2eeJz3VDtYjwYYwxL7zwgunYsaPx8/MzQ4cONZs2bWrqkhzWrFljJJ2zjBs3zhhz5pawRx55xERGRhq73W5GjBhh8vLynI5x7NgxM3bsWBMUFGRCQkLMrbfeao4fP+7UZvv27eaKK64wdrvdtG/f3syaNeucWt58803TvXt34+fnZ/r06WOWLVvmtL82tdRGTeOVZObNm+doc+LECXPXXXeZ1q1bm8DAQHPTTTeZQ4cOOR3nq6++MsnJySYgIMC0bdvW3HfffaaqqsqpzZo1a8yll15q/Pz8TJcuXZz6OOtir4/a1HIxt912m+nUqZPx8/Mz7dq1MyNGjHAED08c74X8b/jwxLGPHj3aREdHGz8/P9O+fXszevRop/kuPHHMxhjz3nvvmb59+xq73W569uxp/vGPfzjt98T3M2OMWbFihZFU4+M99bluCJsxxlh7rgUAALRkHn/NBwAAaF4IHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACw1P8D7mvpUWZDi1EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 737828 district pop vs 776661 target\n",
      "we'll also trim any HD with total islands' pop <= 15533\n",
      "working on HD 1549\n",
      "Out of 37 discontig HDs 37 had discontig HDs.\n",
      "Of these, 0 won't be under 737828 after all minor discontigys were shed, while 37 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 37 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 180 37\n",
      "{11794, 12820, 12821, 12311, 11803, 12316, 12828, 11808, 11298, 11310, 12849, 12852, 11840, 11842, 13380, 11333, 13382, 12872, 12873, 12875, 12879, 12884, 11351, 11865, 11354, 11867, 12380, 12383, 12399, 12400, 11891, 12404, 11893, 11895, 12408, 12411, 12413, 11902, 12415, 11904, 11905, 12931, 12420, 12421, 12932, 12935, 12936, 11413, 11932, 12451, 12454, 12460, 11439, 11952, 12465, 11954, 11955, 12975, 12471, 11960, 12987, 12991, 12993, 13506, 12997, 12999, 13000, 13001, 13002, 12491, 13513, 12493, 12494, 12495, 12496, 12497, 12499, 11989, 12500, 11991, 11996, 13020, 12510, 13027, 12531, 13044, 11514, 12545, 12548, 12037, 12039, 12049, 12050, 11539, 12564, 12565, 11545, 11034, 11039, 12067, 12068, 12070, 11559, 11061, 11062, 12090, 12622, 12113, 11101, 11614, 11104, 12642, 12645, 11622, 11623, 12180, 12181, 12183, 12184, 12190, 12707, 12709, 11180, 11182, 12207, 11701, 11190, 11703, 11705, 11195, 11196, 11708, 11709, 11199, 11200, 11716, 12229, 11206, 11207, 11719, 12233, 11210, 12234, 12235, 12236, 12237, 11220, 11224, 12762, 12763, 12765, 12257, 12770, 12772, 11241, 12267, 12779, 12269, 11246, 11759, 12780, 11762, 11763, 12277, 11766, 11767, 11768, 12278, 13819}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "29d5931e-7878-4366-bb2b-d0cb49774a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #do an intermediate save here -- kernel is wobbly\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"mostlyContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 37\n",
      "swell-generating HD 1549 our 1 th HD we must regrow out of 37 0 sec elapsed\n",
      "swell-generating HD 5664 our 11 th HD we must regrow out of 37 1186 sec elapsed\n",
      "swell-generating HD 7263 our 21 th HD we must regrow out of 37 2163 sec elapsed\n",
      "swell-generating HD 11896 our 31 th HD we must regrow out of 37 3716 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4bdba160-9a32-467c-8876-b8e01de56ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 0.9997 0.07361\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.99947 0.07383\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 1444 time is now 0 sec\n",
      "working on HD 2999 time is now 15 sec\n",
      "working on HD 4146 time is now 30 sec\n",
      "working on HD 5924 time is now 39 sec\n",
      "working on HD 6482 time is now 59 sec\n",
      "working on HD 7033 time is now 88 sec\n",
      "working on HD 7625 time is now 104 sec\n",
      "working on HD 8140 time is now 123 sec\n",
      "working on HD 10265 time is now 136 sec\n",
      "working on HD 10785 time is now 149 sec\n",
      "working on HD 11301 time is now 185 sec\n",
      "working on HD 11813 time is now 201 sec\n",
      "working on HD 12320 time is now 243 sec\n",
      "working on HD 12837 time is now 271 sec\n",
      "working on HD 13604 time is now 319 sec\n",
      "out of 7263 total HDs, there were 7226 0 37 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 325 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "623fb818-67d9-458d-b854-4c70dfa449ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 815494 district pop vs 776661 target\n",
      "working on enclave-y HD 1541 . We have evaluated 0 of 5923 enclavy HDs.Time is now 0\n",
      "Out of 5923 HDs with enclaves 37 had enclaves.\n",
      "Of these, 33 wouldn't be over 815494 if all enclaves filled, while 4 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OPTIONAL if still enclaves -- rerun the CANFILL CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):  #internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "18f46c40-7f28-4714-962f-0b462ff97b8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final ??? check -- any more disco's ?\n",
      "working on HD 1444 time is now 0 sec\n",
      "working on HD 2999 time is now 9 sec\n",
      "working on HD 4146 time is now 23 sec\n",
      "working on HD 5924 time is now 32 sec\n",
      "working on HD 6482 time is now 40 sec\n",
      "working on HD 7033 time is now 49 sec\n",
      "working on HD 7625 time is now 60 sec\n",
      "working on HD 8140 time is now 70 sec\n",
      "working on HD 10265 time is now 81 sec\n",
      "working on HD 10785 time is now 92 sec\n",
      "working on HD 11301 time is now 103 sec\n",
      "working on HD 11813 time is now 111 sec\n",
      "working on HD 12320 time is now 129 sec\n",
      "working on HD 12837 time is now 138 sec\n",
      "working on HD 13604 time is now 155 sec\n",
      "out of 7263 total HDs, there were 7259 0 4 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 162 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final ??? check -- any more disco's ?\")  #run again if we ran above block to fill minor enclaves\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "871cefeb-a42b-4377-9392-507753b6749d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 3625 in-HD and enclave pops are 775248.0 183004\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 12050 in-HD and enclave pops are 647331.0 495747.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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nowhK/G2ICUpldKArIyKiINOlHpc5c+YgNzcXOTk5vsfEiROxfPly35/lcjl27tzpe01eXh5KSkowbdq0K7ZvMBgQGRmJ/Px8HD58GDfffHPX3xFdfbbeA4+1FsmucgxuPoIwqRtVVf8JdFVERBSEutTjotPpMHr06DbbNBoNwsPDfdtXrFiB1atXIywsDHq9Hg899BCmTZuGqd9Z9C4tLQ1r167FkiVLAABbt25FZGQkkpKSkJubi4cffhi33HIL5s2b19P3RwHidrlQ+cE3cOSboXI7oZDqsNT0Cf5v0I9Q75GhqPhFJCevDHSZREQUZPy+VtFzzz0HiUSCZcuWweFwYP78+Xj55Zfb7JOXlwez2ex7XllZidWrV8NkMiE2NhZ33nknnnzySX+XRn0o+4VnEXbaiIqQaqRqRUAiolkTgRsdR1EZPhmD4n4e6BKJiCgICaIoioEuwh8sFgsMBgPMZjP0en2gyxmQmj1e7CzZjdFhySj778cQM+JulJ8/jfGx78HsleFk1K14c9hE3BS9HUvT/x+UiohAl0xERAHW1e9vrg5NfmG1uzDrla8gjbdhpvsd/MjthVQmg9fjwHHvHCBiBOb//E7MF0UIslmBLpeIiIIUF1mkHquvr8d7BwoREmGF67wAwa1Fg0eLxpz/IDQ0EWesoQidPBeCVIAg4185IiLqPva4ULfZnDa0lB7A9t2l2F2vhSbVjco6PdQjHNgbMxvL46sx6udLkMFLd0RE5CcMLtQt7oYGrP7nzXArPVgiWQC9kIpTVi3U4V6ElTkRowjFwYTxSGdoISIiP2K/PXWJ2+uG2+vGqVdWQPQ0INwegdqa0ximbkFEgxTXRR+Eo2IEtEo59rZwdW8iIvIv9rhQlzz+8eNQlApYUloN5WQlagsK0KCaiTCVDNtX3oj694fgiLsCx3RyLJnC9aOIiMi/GFyo045UHEXiiRgo7Q1wOuyod7sgC5XgR8NjoJnxA0gkEoQtHgKrEthVXIOVaZzSn4iI/IvBhTqlrKEY+58/CIlchlB3DMwZAn76MRByx3wkzn8cUKgBAJIQGZb9IA3LkBbgiomIqD/iGBfqlCN/vQtJ9So4RTtihFjYHHGIfmwZrrv9L77QQkRE1NsYXOiKHM5aGAsdsIQJSG4OxVFpATynLRg96IeBLo2IiAYYXiqiK9pwvghjBAXKXVZc707ANKMAeWwEFFGpgS6NiIgGGPa40GU1uT3IadHDOViKSSWfwDtKREJcJqITYgG5KtDlERHRAMMeF7qsaqcbcSFabLruVjw4zobJS28BhCWBLouIiAYoBhe6LFltFSrsHmhjFmLUNQkQBCHQJRER0QDGS0XUofys/dj10rMYlJeDOKUcBjlzLhERBRaDC3Xo/LGjmPffDyE5ezd+NyQ+0OUQERExuFDHvB435KoQ1JWXBroUIiIiAAwudBkRyfHYu+UfmHHrTwJdChEREQAGF+qAx2OHO2wLQmKLMG7eokCXQ0REBIDBhTpgseYiKmoOdKllkHJQLhERXSUYXKhdXo8dUqkKTU35EEVPoMshIiICwOBCHbA3JOHrjSFQyxZCIpEHuhwiIiIADC7UgbMHWjDqmjC4a+4MdClEREQ+DC7UrnM1NhzSD8fxL2yBLoWIiMiHoy7pEqIoolDmhVjYiAWLkgNdDhERkQ97XOgSFrcH9oxQyAqsKBmmCXQ5REREPgwudAm514savQS4NRnTEo2BLoeIekFLTg5ElyvQZRB1GYMLteHxeLBl81twVZSjxO7E4BBloEsiIj9r2r8f1X/5K+o2vh7oUoi6jMGF2qiqqsKgQYMwt+wsNo1KgSAIgS6JiPys6dAhRD3+OOrffDPQpRB1GYMLteFwOKBSqVBWVga32x3ocoioN4giIBHgbWkJdCVEXcbgQm1ERkbi/PnzSE1NhUKhCHQ5RNQL1OPHo+a5vyH83nsDXUqXuBvsEL1ioMugAOPt0NSGTqdDWloaQkNDA10KEfUSzaxZgEQK9aSJgS6l05wVNjRsPQv1uEjork0MdDkUQOxxIR/R64XHasW4ceOQnMz5W4j6K0EQoJ05AxJl8Ay+d+Q3wLA4FU1ZpkCXQgHG4EI+Nc/9DSV33Q13XV2gSyEiakN0i5AopHDXclzOQMfgQj72s3kw3vojWL/4MtClEBG1IY/XwvpNOTTTYgNdCgUYx7hQq4bzEMqz4SlPgrOhBaG4LdAVERH5qIaHwtvkgiotLNClUICxx4Vw7NgxlG77E4SwBDhzMhF6G0ML0dWq5fhxWD75pNP7210evLa7EOaW4J4lVxAEaCZEQ6qRB7oUCjAGlwHO6/Xi2LFjcDeUQPvAzxEi5kI1enSgyyKiDtT87f9g/uhjuGtqOrX/1sOlKKptxut7i3q5MqK+weAywNntduh0OtS7lDDXZCN0+Z2cLZfoKiW6XBDUIRBkMlh37uzUa05XWfHAdYPxfzvze7k6or7Ro+Cybt06CIKARx55xLfNbrdj1apVCA8Ph1arxbJly2AyXf72NZvNhgcffBAJCQkICQnByJEj8eqrr/akNOokhUKBphY7qpJuQlzl58D4uwJdEhF1RCoFXG7IY2OhSEnp1EtC5FJUWx2YmsqxIdQ/dDu4ZGVlYf369UhPT2+z/dFHH8VHH32ErVu3IjMzExUVFVi6dOll21q9ejU+/fRTvPXWWzh9+jQeeeQRPPjgg9i2bVt3y6NOkslk2Fmnx9q8SDTe8REQPTLQJRFRBwSJBFKjAS0nTiBk/PhOveaGkdF4bOsx3DktpXeLI+oj3QouNpsNy5cvx4YNG9rMsGo2m7Fx40Y8++yzmD17NiZMmIBNmzZh3759OHDgQIft7du3D3fddReuu+46pKSkYOXKlRg7diwOHTrUnfKoC7xeLyqtbswZrMPnpzixE9HVLvbpp5G4fj0knVySY2pqOD54YDpuHMPbiKl/6FZwWbVqFRYtWoS5c+e22Z6dnQ2Xy9Vme1paGpKSkrB///4O25s+fTq2bduG8vJyiKKIr7/+GmfPnsW8efM6fI3D4YDFYmnzoK47f64MqS0qlFbWIt9kC3Q5RHQFglwOqVbT7s8cbg9uXb8ff/vybJvtRjXXHaP+o8vzuGzZsgVHjhxBVlbWJT+rqqqCQqGA0Whssz06OhpVVVUdtvnCCy9g5cqVSEhIgEwmg0QiwYYNG3DNNdd0+Jq1a9fiqaee6mr59D0nv65CuFOG4krg1h8nBLocIuqifJMVdpcXYxIMyC5uwJy0KHxTyNmvqf/qUo9LaWkpHn74YWzevBkqlcpvRbzwwgs4cOAAtm3bhuzsbPz1r3/FqlWr8OWXHc/gumbNGpjNZt+jtLTUb/UMJCfONcNkcCLDo8fIOEOgyyGiy6k5C7idvqeiKOK3207iTztOw+H2oLHFhTCNArvPdu5W6e+yNdTjnScfw/njOX4smMj/uhRcsrOzUV1djfHjx0Mmk0EmkyEzMxPPP/88ZDIZoqOj4XQ60djY2OZ1JpMJMTEx7bbZ0tKCJ554As8++yxuuukmpKen48EHH8Rtt92Gv/zlLx3WolQqodfr2zyoa6qtdkikUjjdIuKT2+96JqKrRE0e8J8HgK9+79tU3+REnDEEEgmQXdyAlHANjpeZcc2wyC43f/bAXmQsWIzjOz/1Z9VEftel4DJnzhzk5uYiJyfH95g4cSKWL1/u+7NcLsfO78wvkJeXh5KSEkybNq3dNl0uF1wuFySStqVIpVJ4vd5uvCXqrOJyE6AwYZBJjsQZqYEuh4gup2g3cM1jwIkPfJtkEgncHi80ChlCFFKMiNUhVC3Hylld/zw3mxsRnpiMswf2+rNqIr/r0hgXnU6H0d+bVVWj0SA8PNy3fcWKFVi9ejXCwsKg1+vx0EMPYdq0aZg6darvNWlpaVi7di2WLFkCvV6Pa6+9Fo899hhCQkKQnJyMzMxMvPnmm3j22Wf98BapI6Wnc2AyeuBVmzFxfPs9YkR0lfB6AIkMsJT7NhnUctQ3u+BweTAm3gBBELB63vBuNa8Lj0T56ZMYMfM6PxVM1Dv8vsjic889B4lEgmXLlsHhcGD+/Pl4+eWX2+yTl5cHs9nse75lyxasWbMGy5cvR319PZKTk/HHP/4R999/v7/Lo4scVjhampCUPAiNx3dCIeMkykRXlbOfAZ89Ady2GYhKAxImAnueBTJ+2ma3Z28dC5fHC5m0Z5/h4dNn4fNXn8eUJbf2qB2i3iaIoigGugh/sFgsMBgMMJvNHO9yJce2ALv/F18af4zEyYthMpkuewcXEQXAv+4Fxv4YKD0IzP5167YzO4CkqYCas+BS/9HV72/+N3sgOvMxsOivmCjLw86dOzFq1KhAV0RE3+dsAlR64MAr325Lu5GhhQY8v18qoiDgcQNSJYzVh/DAw68Fuhoiao8hESj8Cpj720BXQnRVYY/LQBQ2CDjxL+DaxwNdCRF1ZMbPgeY6YOztga6E6KrC4DIQzVwNGBKA0csCXQkRdcSYBNz4v4BSF+hKiK4qvFQ0EGkjgZmPBroKIiKiLmOPywAjiiIsNdWBLoOIiKhbGFwGmJzPPsaHf/49Ks6eCXQpRHQFzR4vbs8pxPtV9YEuheiqweAywJw/fgzX3XUf1yMhChBvS0un993TYMXcCD3eNzX0YkVEwYXBZQBx17ZgVONECCVu1JacD3Q5RANO04GDOHfzLbDt6dx6QNVOF5JUCnxVb+3lyoiCB4PLANJyshaNiY2wHizHpB/wjiKivmb9/HPE/fFpNL73Xqf2j1cqkNdkx9xwzgZOdBGDywBSXFWKXMEGhUOLoVPaX62biHqPt6kJsqgoWL/4osN9HA4TTp/5NZzOeswK1eF0kx13x0f0YZVEVzcGlwHkeEM+0rTJqBzsgEQiDXQ5RAOOcthQ1L+1GZGPPNzhPqVlb0KljEVFxbuQSwS8PDKZPS5E38HgMoCoo47CZdiEnOazgS6FaEAy3nYbpEYDQn/84w73aW4uRlTUAhSe+0sfVkYUPDgB3QCiUp+BVDsKU6bFBboUogFJqtUictWqy+4jlxtgt5cjKXFFH1VFFFzY4zKAGI0RaDQXwGAoDHQpRPQ9+wprYW5xISZmCc7mP43omB8EuiSiqxJ7XAYQlUqGtLSZiIpcEOhSiAitM1mLoog8kw2vZp5DrF6FZ344CdOmdjx4l2igY4/LAHHU0ozDqlvhcJig040KdDlEBODzzz/H66+/jkPnanHXtGT8O6c80CURXfUYXAaIPxdV4oB7BIypf4Ig8LQTXQ1qamowbNgwNDTUwysCWiU7wYmuhN9gA4AoipALAlQSCbZVNwa6HCK6QCKRoKGhAWGeemzYcw6LxsQGuiSiqx7j/QDgrqmBo7YWw5MTMFyjCnQ5RHSB1+uFKIpYOCMD450KpEZqAl0S0VWPPS4DQPVf/gLD+SLsq6zBdKM20OUQ0QWzZs1CdHQ0IiMjMTreALWC/5ckuhJ+SgYAj8WKW8pMSCk6A9XMsYEuh4guSE5ORnJycqDLIAoq7HEZAGosdpTb3MhtDHQlREREPcPgMgAUhxnwcew7+HO4NdClEBER9QiDywCwd95IXDf8bgjxZYEuhYiIqEcYXAaABCEfHx5vgEN+MtClEBER9QgH5w4AkxqOwzUkCpWKMYEuhYiIqEfY4zIAHIMDO5pO4bYWd6BLISIi6hH2uAwAp8QWPBA+HnXhQwNdChERUY+wx2UAiK28DbVnDMjyWAJdChERUY+wx6WfM9e0QCIxwNWUjGXDhgS6HCIioh5hj0s/Z6u3Y9LwsVBUhmJYyIhAl0NERNQj7HHp50L0CqgdeiSNkkGllQe6HCIioh5hj0s/Fxqjhr3JhdjBRgiCEOhyiIiIeoQ9Lv2cIAi44Z5RgS6DiIjIL9jjQkREREGDwYWIiIiCBoPLAFBpq8TJOq5TREREwa9HwWXdunUQBAGPPPKIb5vdbseqVasQHh4OrVaLZcuWwWQyXbYdQRDaffzv//5vT8qjC/5w4A949vCzsDqtgS6FiIioR7odXLKysrB+/Xqkp6e32f7oo4/io48+wtatW5GZmYmKigosXbr0sm1VVla2ebz++usQBAHLli3rbnl0gdvrhkwiQ4wmBnvL9wa6HCIioh7p1l1FNpsNy5cvx4YNG/D000/7tpvNZmzcuBFvv/02Zs+eDQDYtGkTRowYgQMHDmDq1KntthcTE9Pm+X/+8x9cf/31SE1N7U559D0iRMglcsglnMeFiIiCW7d6XFatWoVFixZh7ty5bbZnZ2fD5XK12Z6WloakpCTs37+/U22bTCZs374dK1asuOx+DocDFoulzYMuJZO0ZtNiSzGmx00PcDVEREQ90+Uely1btuDIkSPIysq65GdVVVVQKBQwGo1ttkdHR6OqqqpT7b/xxhvQ6XRXvLy0du1aPPXUU52ueyB7avpTsDgsUMvVgS6FiIioR7rU41JaWoqHH34Ymzdvhkql6pWCXn/9dSxfvvyK7a9ZswZms9n3KC0t7ZV6+oMwVRhSDCmBLoOIiKjHuhRcsrOzUV1djfHjx0Mmk0EmkyEzMxPPP/88ZDIZoqOj4XQ60djY2OZ1JpPpknEs7dmzZw/y8vLwX//1X1fcV6lUQq/Xt3lQWy6viPMtjkCXQURE5DddCi5z5sxBbm4ucnJyfI+JEydi+fLlvj/L5XLs3LnT95q8vDyUlJRg2rRpV2x/48aNmDBhAsaOHdv1d0KXWHuuEvefPI9zzQwvRETUP3RpjItOp8Po0aPbbNNoNAgPD/dtX7FiBVavXo2wsDDo9Xo89NBDmDZtWps7itLS0rB27VosWbLEt81isWDr1q3461//2pP3Q99xttmOexIi8C9TPX45KDbQ5RAREfWY3xdZfO655yCRSLBs2TI4HA7Mnz8fL7/8cpt98vLyYDab22zbsmULRFHEHXfc4e+SBiwBgNMrotzuCnQpREREfiGIoigGugh/sFgsMBgMMJvNHO9ywc9Pn0eTx4tViVEYb9AEuhyioLO1qh4T9BqkqpWBLoWo3+rq9zfXKurHnhwchyVRoQwtRN1wytaCL+sseLqwItClENF3+P1SEV09IhVyLI4yXnYfu82F/MMmFB2rgbnWjharE2qdAobIEAwaF4khE6Kg0vhnxl2H2wOlTOqXtoh62wlbC26MNOC/T54PdClE9B0MLgOU2+XBkc9KcOTT8/B6RSSmhWLwuEiE6BRotjpRV2bF7nfy8M3WfEy4MQUZNyRBKut+B90/9xfjXF0znliYBrmUHX109VNJJGjyeDBc0ztzVhFR9zC4DEANVU3Y8UouLLUtGHdDEsbOToRar7hkvyazAzlfluLQR0U4e8iERQ+MgSGy67Pv/uObIpyqtOCszItalxux0kuPRXS1mW7UYvnxQswK1Qa6FCL6Dg7OHWAqCxqx/ZXjUOuVWLByNMJirzz+pa7chk/W58LZ4saND6QjZpCh08ezuzzYuOccIqJCYHY7kJFgxCSxDogc1pO3QdQn6pxuGGRSyCRCoEsh6rc4OJcuUVO7E0dz7kblORO2PZ+D8Dgtlv6/8Z0KLQAQHq/Fsl9OgCFSjW3/l4PaMlunj/1edjEUcgkq396Mo1kHkLTlSaDs0nWuiK5G4QoZQwvRVYbBZQCoqHgXGsUC7Hg5F+HxWtz00NguD7gN0Spw08/HwhAZgu0vHUNTY+dm46327ofFew7XpdvxjFgAa4sdX7WUd+dtEBERMbgMBF43kLXVCBE23PizdMgU3buzR6GSYfGq1uUYPn3tBLzeK19lXDh4DIaG1eO01wvHLbdhd2Qp5ElXXv6BiIioPQwuA0DZ0VEwV8kx48eKdgfhdoXGqMS8FaNQVWRGzhcll93336YGlOQegqL4IL4WKvD6h6+jzBYOFRSoaa7pUR1ERDQwMbj0czWlVpRmj0H8+JMo3n8K+Qf39bjN2CFGZMxNwsGPzqG+oqnD/eKLd8JadA7Hym1IMcsRKTNipGIw9mRvR24hx7kQEVHXMbj0Y6Io4put+TBGa5Ax7XoYomJx/KvP/NL25B8Mgi5UhW/ez+9wH7M7EQtvfQyz4lbhpqQxGDsyCaHKUVh98+/hPtj5O5OIiIguYnDpx87n1qH8bCOmLx2MpoZahMUloDgn2y9ty+RSTFsyGCUn61F6uv6Sn+dXNsKriYFWZ0SV9STCzgvwVrohiDJ89oufI97h9ksdREQ0sDC49FOiV8S+fxcifngokkeHQ20MhaW2BinjJvjtGKkZkYhJ1WPfBwVoMx2Q14Mh1Z8htnYvRK8IQ7QRWlktHGVGmM6eQISggtHlQT+ZQoiIiPoQg0s/VXS8Fg2VTZh6cyoEQUDCiFEoP3MSQydP99sxBEHAlJsHo7bU1rbXJXsTMOImlNWa8fZ7/4SmwIG6sKnwes7jtqd/B0NsMtTJBnhtLr/VQkREAwOn/O+ncr4sQexgA2JSW8eSSGVyLHn8t34/TvwwIyKTdMj5ogRJI8Ph8nix+1wz9KUbMG3OrTh3JBMnj2hQXiVHZHMFjr/2CmqOl0BUpSGkuhlSHaf/JyKizmOPSz+wu96KbxqsvuemYgsqC8wYNzep148tCALGzU1E6ekG1JbZ8M7BYsy45X4Mmr4UoZGxqDvrQerIEDQ2uBEdNQzpK38G4/gZqLdJAbe31+sjIqL+hT0uQSKvyortxytanwitU5ALF/5YEyLBopHRqHa4EKWU4+TucujCVUix/xv4vBSY94derW3whCjse78A2V8cQnb8N7hT+SRUMYkAgIihqSg/VAS7IwbOehuqXvkPbNZaaGUy3/sgIiLqLAaXIFBtsWP53w/C6fZAq5RBBHBxXKvd7UFdiASPjk3EN2YbFhp0KDhSjXGzEyAp+AugiwXMZYAhodfqk0oliBxej7M5IYgbnIIqawNidKEAgLTpE3D660roNS1I+dlPYDpWifJzB+CxncColom9VhMREfVPDC5XodwyM37/8Uk0OTwAgFqbAxIB2PmL6xCpU7bZ97OTVfivT05AIgi4LlSHHXtK4bJ7MDxDC2RFAtUngYKdwIS7erVmp7oIEscYlJV4sVlbgl+Mbw0uar0CCRlShEtkqPyoDPWVu6AvLEG+0grzkTKox0b1al1ERNS/MLj0onM1Nmw7VoGRsXrMTouCTHrlIUV78mtw/z+zkRyuwcSU1i9/iSDg1omJl4QWAPBcWC9IKhFgkMvQlFOPsDg11Ao10FIPxGUAg67x7xtrR8rwCajMNWNweQxco9pOLhca44bWLkHM/DQ0r/4bJJFGhMlDoJ+S2Ot1ERFR/8Lg0ks+P1mFlf/8drK3FTMHYWSsHhv3FmHOiChcNzwK4xKNKK5rwgdHypAYqoZcKsHj7x/HrKEReGn5eKgVVz49bq8IiIBMIsDZ4oal0IKkKCUatp9HVOpEwFwKhA3qzbcKszkHISERUOsKISlLwilbGYCU7+whQqaSo2rXxxBLS1AVqodeJoe3wdmrddHVRxRFCBzbREQ9wODSC6qt9jahBQA27i3y/bm8sQUvfFUAnVIGq8MNnUoGq711JtkfTUjAn5aOgbwTvTMAfCs0SyUCSk7UwesRoR5hhPN4A7w/+X+QdHMl6K5oaipA3pECxIcMwVmXiCk15jY/LyiXYUZkDQRDGMITU3DjtKkISRkE3Yz4Xq+Nrh5Hjx7FN998g5/+9KcwGLjkAxF1D4NLL3hlVyEAICVcjf+smonj5Y346cZDvp/vXzMbpyst2Jtfh+RwNRaMjkFBtQ15VVYsHR/fpf+Rur8TXIqP1yI0MgR6lQRSvQKCpG/+Z1vabEGo3Ykh4YNQpKqCquzbsPRxZjFGz7gO0SlGFB+vhXGRBZYdO2D46Z19UhtdPU6fPo3Zs2fj9OnTmDp1aqDLIaIgxXlc/Mzc7MKb+8/j9kmJeP9n02FQyzFraCSK1y3Cipmtl2zkUgkmJIfh4blDcUtGPFRyKUbHG7BsQkKXu9E93ta5UASviOITtUgZFwHZ8VJIo1QQZL1/es80teCrMhGWEj1c5eUQw+pgPS+DKIo4eNwEg1aBYSlGAEDsEAOax1yHpL9vgER56Xgd6v9cLheOHTsW6DKIKIgxuPjZrrPV8HhF/HzOUIRr2345//rGETj+u3mdvgzUGW6vCIkAPPfVy3A0udEQVo6cmHzUjuub3haDpQp5tiEoVyXj44ZKzJx/PTxNEpzKrUF5dRNmTYjz7atUy+Fo8Vyxzcr8POx6cwO83ivvS8FDoVCgtrYWM2fODHQpRBTEGFz8SBRF/Or9XKRGaBBnDLnk5xKJAL1K7tdjerwipIKAxiIHIBGxz/IJJk2fhZHjRvv1OO0RRRGHi9dhgetTaGOiMXzJYgxJj4UoAJmZpVg6N/WS12iNSljr7Zdt9+CHWyGVK1By4nhvlU4BcO2118Lj8WD48OGBLoWIghiDix/tP1eHFpcHM4ZE9Nkx3R4RglwCbW0UXOEWlLaUISwssleP6Tx/HgBgyt6IE4UV0KgqkC82oDJBCWWIDNIwJVLV7V8KikzSobbMdtn2Ra8HClUIjuz4j99rp8CJjIzEvHnzIJNxaB0RdR+Dix8dLWmEVinD734wqs+O6RVFSEQgpCYc9qh6pDiSER0d3avHtO78Ck0HDkBWaEFLYxXyTrvw42UTUeZworKxBeoEDUyF5g5fL6D1bqijlmb8uagS4sVpgC8QvV5ojKEYOev6Xn0fREQUfBhc/MhidyFcq4C0j+7mAVrHuBjcgNwRgphUA0a4muA6+m6vHjPsrjth2/sNHK5JuNZ6A0ZrpmOYPhozjDr88WQZpk2Iga3egRZr+/O0xA01ojK/EX87XwWr24OD5qY2P0+dMAU5n29HytgJvfo+iIgo+DC4+JGlxeX3MSxXYgiRw2BrvbPoh9MXQ+1pxp5CG3bv3t1rx6yp/wo5k5Jw1lSBomE3Yt59DwAAdDYPlkUZEZWsb92v1Nru6xUhMrjdXogioJdJ8Y/y2jY/HzfvRvxk7d+g0mp77T0QEVFwYnDxI7vLC5W8b3+lUwaFIdIhQhoihcaohMxug6mhGWK9A5VlFb1yzOaDJjTLHdiXEA291gWHs7Vn5UilBdcPjYQhMgRylRS1pe2PZRFFEQIAtygiRinHgghORkZERJ3D4OJHKrkEDre3T485KEKDRJcUdq0UgiBA2zQeaocdqkIbLEUFvXLM8ISZ0DQehCMqHF6rA+9v+xLbT1Zi7oVByYJEQESCFjUl7fe4iF4RgkTAdKMWb5bX4fowXa/USURE/Q+Dix8pZVI4XH0bXARBQKxLwDlJ65wnu8RKyDUHUSk7DnNVEVy2jgfJXrSxrAZvnC1D/vYjsGVmoiUnBx6PF5aWS8eoOBxWWM6VwlOuQULNaSREDcO0JilMOQVIDlf79ouI16KuoumS1wOA1yNCIhHwYHI0Pp84DAY57zIhIqLOYXDxI6lEgMvTt8HF4/ZC6fCiSHTB3OzCkJomhH+pQUWFFrVnsuD6+OXLvr5hVzGaTU3Yl/kG6kurse3LL1H2SQ7Me/+BLf/5CEW5386l4vV6sXnz66hU70JMiBzS0nIkSZsRHqHHLIkHzdUNvn0N0WpYalpQU2qF3eZqc0yvR4RE2jqAmQvuERFRVzC4+FGsQYVK8+UnV/M3S20LIAL1MuBgUR0aooeiUBKGGs1haIwKiDUFsJ8+3e5rq5us+ODYp/CeOot6Vywa5Gr8M2M66sKacLruG1hzSrH944/xVW4pyqxlkEgkUKu9cKsNaKypQaSrAe5aC3SzMxChkuH8F0d9bRuj1PC4vdi1+Qx2vX2mzXG9XhESP84eTEREAwe/PfyouK4J+pC+vezRaGoGAAhGOfYU1gIjRHiMIVisWoD0lDTIp/4IrsoqfHGszNcbJIoiLE1FKC36E2oNtRjc+CHSqlTQmySAqENWVBkOR4dCMuwzmJInoOHcWWw88AZKSnfBKDsD0/4qTEkdhbiK87BXmSHVqCG63JDIvl1c0RjdOnNwdIoeZWca2tT83R4XCqza0vMoP3Mq0GUQEXUag4sfHS5uwLXDenfW2u8z17RAppBgQqweu6rMSB02GlZFMkotYZAf+wDy0TMhHT8Br+/fguyyEwCAYzXHkHXsTdjqS3Ct8Wscj1Bg6vlvoJG4IK+og0ISg6PlCRA0gyAWnoO7aAeMLVUozj2EYdIYXD/5BgiiB0XDWiBrqgQASEIU8Nq/HROjC1NBkAhosbng8bSdYM7j9kIiY3C5Gnz9xgbs27oZTntLoEshIuoUBhc/8nhF6Pp4HhdbvQO6MBWuGxKJYksLJiZcC4feivTJERBED4QQLTbtOw61rgkheyrhaKnB+X2Z0OVkIP94Ak5JjRjprMOXS5bgG0U9kmtqEC1+hsGOcGisN2KF9jRy4wZBM2wxIA6GWeJF9TN/hmXvYcSmjkNJS+v0/5ppIyEWVMHrbh0kLJFKoDEoUHXOgqSRYW1q9npESC9cKrJYjqOw8C8Qxb4dG0SAs6UZar0BMoUCJblcsZmIgkOPgsu6desgCAIeeeQR3za73Y5Vq1YhPDwcWq0Wy5Ytg8lkumJbp0+fxg9+8AMYDAZoNBpMmjQJJSUlPSmvz3m8Yp/OmgsATWYH1AYlpqVGACLw4q6DGKr5BkkLxkDz5BcAgDKlBEPEGdBGymDK3Qn9p/Wor7fD5dHDfqIZoVbAWPg+HNEmjG8sx5DsBrg8X0NmUeAzTRwGDbseuXXA0XMRqCycAsPNN8N0+3JEx8+ADHLYC8tg3nYQhqlD0HCm1FebNlSJ8HgNrr2j7aJ6HpcX0gvz3RQVvwSv14nGxkN990sjAIAgkcDrdkOp0UKp0QS6HCKiTul2cMnKysL69euRnp7eZvujjz6Kjz76CFu3bkVmZiYqKiqwdOnSy7ZVWFiImTNnIi0tDbt27cLx48fx5JNPQqVSdbe8gPCKIhwuT58es8nsgNaoRIxBhXGuKjTb5Ji4eDVOb17t2+eIU4ZzxlAcbz4Hp6MOmDYZH8fkID++HpOPWhBRmoJr1c2ojklCrUsGiXcaGiSJ0EWa8VptBqYYwhDfbMSKFdMhS4lG3ZkCRBbW4+ihbRDVMli/zEXEvfMRM3ci6k5/G1w0RiU8Li/UekWbmm2Ndqj1CriqmyE4pJDJdCgt+2ef/c6olVypgtvlhLW2BnHDRgS6HCKiTunWSFKbzYbly5djw4YNePrpp33bzWYzNm7ciLfffhuzZ88GAGzatAkjRozAgQMHMHXq1Hbb+/Wvf40bb7wRf/7zn33bBg8e3J3SAuraYZH46Hglnlw8ErI+umumqdGB2MGtM8+OGzYSJ4/lIvbHd6Gx6Btkv3or6ptGY/6oxThbUAhVRA6yVGEQymyQJyYhp86ASeH1aHaIGOlMRG1hGaYmD0KlZRhG1VsQWduAVZ4avPKeFdPHxkKrlmPe0kk4uPvf0O4/jGHTh8F47SSEJ7WGV6lSDu93bgfXGJSor2y+pGavRwRa3Gj4IB9qcTIUN4cgKiS5T35f1NbCVb+A2+WElCs2E1GQ6Na366pVq7Bo0SLMnTu3zfbs7Gy4XK4229PS0pCUlIT9+/e325bX68X27dsxbNgwzJ8/H1FRUZgyZQo+/PDD7pQWUDeMjEF9kxPFde1PvNYbmi1OqPVKAMC4QVGoMzehxenBiBvXYML972HIgvGYVL4D13prYT8kQU1tCrJCkzG4wY4UaRMKMuLQkrQXZ8rKESo0IjqyHGPEFzDUVY5C3Vnok8IQ0dSCWuh9x1TeuAjShx7FuFt/hpRhEwEJ4Cwru6Q2tUGBZrOjzTZ7kwvKEBmcJVaoM6KgrE5Gack/EBo6vXd/UdQulVYLbWjYlXckIrpKdDm4bNmyBUeOHMHatWsv+VlVVRUUCgWMRmOb7dHR0aiqqmq3verqathsNqxbtw4LFizA559/jiVLlmDp0qXIzMzssA6HwwGLxdLmEWjjkozQKmV460DfjM3xuL1wO71Qqlv/txxrUEGEgGrrt3PJDBq1GDPueQKxQg1qkscg/qQHDrsEakUFbpdVIkQYj6FCBKp/mA4hzANXsxentBrsSzDCHa9BfowSGnUDjpdv9LWZPnca8vdk+Z5Xr3sGFb/4f3BVVgL49g4ipVoOZ4sbovjtNlORBdGpBkBsnfbfg2ZIZSEoKn6hF39TRETUX3QpuJSWluLhhx/G5s2b/Tb+xOttvbRw880349FHH8W4cePwq1/9CosXL8arr77a4evWrl0Lg8HgeyQmJvqlnp7QKmUYHKVFi7Nvxrk4W9wAWldbBoAofes5qfreJHgSiQRTVq5Cc2IJjIku3GSpwFxdLhQqCyRw4VDEZAxTDsaQECOqHOEojZwEUV6NLKsUXlU1klP3I1miRHZ5oa89hVYDW2NrWHSVl0N/002w7NgBiUQCt7N1plxliAyiCLgc3/4+vN7W6f7liTo0H6+BZ1Q54uN/jPr6vV167+eO1uD415f28hARUf/WpeCSnZ2N6upqjB8/HjKZDDKZDJmZmXj++echk8kQHR0Np9OJxsbGNq8zmUyIiYlpt82IiAjIZDKMHDmyzfYRI0Zc9q6iNWvWwGw2+x6lpaUd7ttXmp1uFJisSAgN6ZPjOZpbg4vyQnCJMVwILpZLZ+8N0YQjIyoRe7wVyIw7gvxkIEKfjVudO1Ahb0JuwSlEnY2ABm7o5CPg8EZAPiQGqZ48RNXFwObKQrTz23laJtw0G8c+/rr1iSBAdNjhrq5G+PBY1OUWA/g2UF2s01zTDH1Ea40ygxL6OUlQTQhD8TELWlqKO/WeRZcLpQ88gPPr30JFfiOaLZeup9TfeL1i6wzJRETUteAyZ84c5ObmIicnx/eYOHEili9f7vuzXC7Hzp07fa/Jy8tDSUkJpk2b1m6bCoUCkyZNQl5eXpvtZ8+eRXJyxwM2lUol9Hp9m0egfXqiCk1OD27JiO+T4zntF3pcLlwq0iplUMklqLY42t1/+oyfYH5SEfbHzEWtehJc1TNwtsyIZHsRBsfOQEGzEZWKXEjtB5BS3IjyhmEori2AqnY8/rssEqa8by8PyZUKeL0iPC43JGo1mo8chWHpMoSOGoTGwtbLghcvYV3sGWqobEZ4nNbXhjLFAJkiDS3ur+BubH/gNgCI35nArvnwYShGj4Xx/CFY6+0ozq3tzq8uqGR/UoxPXzuBunJboEshIgq4Lt1KoNPpMHr06DbbNBoNwsPDfdtXrFiB1atXIywsDHq9Hg899BCmTZvW5o6itLQ0rF27FkuWLAEAPPbYY7jttttwzTXX4Prrr8enn36Kjz76CLt27erh2+tb7x8pw9TUMCSGqa+8sx847a2XYOTK70y1r1GiqvHSO3kAQC7XQGVOx4NJQ7Cj0Y1BNjdM8nCEl50HvHXQwA63PA7lbidGyishtxfgaK0WhbJmjPKKqDi8G6YRsxGdnAAAGH3jdcj5+GukP/447CdPQDV8WOuBvG3r8l0qameKG5ctHlrVfBz7WgH7fBdUmrYT+DnONaL+X/kI++EwKFMNcNfWQpEYD1VRDqLv1iM0Sdnt31+wMBVbMHFhCs7sr8SMHw4NdDlERAHl93t2n3vuOSxevBjLli3DNddcg5iYGHzwwQdt9snLy4PZbPY9X7JkCV599VX8+c9/xpgxY/D3v/8d77//PmbOnOnv8npNtdWOfYV1WDY+oU+OZ3d58EFW6xiPHScrYbW3jisxGnSwVBW3+5qSLz9H8uQf4LqxIzH4cDU8Fc2QKeSYFrscEZLzkEbaUTMiGSX6eOxIbcaI2KMYZTRDliiHTQxFmCIVZ3ce8LUXGmFEi9kKeXQUdBduf/+ui+sReT1eNFucCNEqLtnH0VSNk9tPIDIuznfJC/lfAuVHAADNx2sRunQomg639uLIY2LgPn8ejsEZ2Nn4Ce47fBfOmc/52tt+vBL5Jmvrce1u2Asbu/Bb7R1/PPBHbMzdeOUdOyCg9XJRYzUvFxER9Ti47Nq1C3/72998z1UqFV566SXU19ejqakJH3zwwSXjW0RRxN13391m27333ov8/Hy0tLQgJycHN998c09L61PVFgdEERgeo+vU/u+ceQdr9qxpc8dNV7x3uBQfHm0NLn/85Az+8lnrpbbo6FCMLf0XCj58CfWnD7Z5jaWkBGFpIxEeagQ8cXArDZh7/09hixUQIrVAj0Ownz0FjVaNEtlUVFumYKl8AqJiCuHVhUOVVYDQc1a0ONy+NuOmZODs7rbHgdAaWC6uAO3xiKgsaERkctvfTam1FBs+XANRlQi3/QQEiQDYzcCBl4EvfwegNXxIjUo0H6kGAISMHw9XaSlSf3436ocW4JeTfont57a3tlffjG3HyrH2k9bVqM2fFMGaWQZnAC+xVNgq4PK6cNh0GB5v9wZta4xKnDtajfTr+yYUExFdzbhWkZ+EaVp7E+qaOjdYdFfpLkSqI3Gm/kyXj+Xxiti4twhaeeulGC+A9w6XodbmgCYsBkfVYzDkllUQnU0o+PAllO35AJbiImiio31tTNfvQrm7DOdyauByOhBSG4JhZgOubxqEtIaDuDNExLbTcWhqGIQD59NxWO2CfskNiPjZMhQc+3YJh9QRg2A6V96mPqlMAlezw9fjUltiRUSiDpLvLYfw8bmPkSqJQ+nU86ivexe2pnygNh9ImgaUHgRsNZBHqtGcVQX9vNbxToJUirhn1kE/fx5EUYQoisgsbb1t/nSlBdcPj8JXZ1pDjsfshHpsJJoOVXb5d+wv1c3ViNfGY2/5Xpiar7z0RXum/CAVyWMikJAW6ufqiIiCD4OLn5hbWi/V6JRXHjYkiiIUEgVKLCX4uvTrLh/ry9MmnK9rxv2zWmcX/seKyZBJBKzPLES0IQSNzU6IoojwsbMx5JZVCB+SjqKtryLCWgTR0XrHUVradCTNCEP+kQKEOsJgcEth1SYgxW2GMfYGyNGIBRMLkGneh2g78FZlBp4sUWLfnhKMmhDrq6W43IISUY+q0wW+bREjE1B9rMg3i66lzg5D5KV3WtW11CEqIhEn83dh2Dwl8vP/CMiUgKsZiEwDFGpop8VCBKCdGnvJ6yNCIrC/cj/+K/2/AABKuRTNTg+GRmnb7CfIAvfXPFodjXJbOWbEz0C0OvrKL2hHiE6B4VNiIAhcUZuIiMHFT/KrWy9HDI7UXmFPQBAEOL1OJOoSMSdpTpeP9fc95zApJRRDo1uPlZEUintnDsKb+89DKhHg9ohobHb59g+JHYJRP1qJArsKn//2FljfegZehxNlsgp4077AO94WfDZ2IfKNzTgny8UXZTkYP3Elbph+K6TTwuAaXIbrBBNe/dVSxKZFYPMnBXjv80K8tf0sCssskMcPQv7Bb1cX1g9Lgq3EhPqq1hmEpR0sfxCjiYF1pA7Xt0QiLnEB6uv3wBOe2trbYkwEFBpI1HIYFw6CRH3pqtv3j70fYaow3+9wfJIRHx2vQEaSEQAgC1Oh6bAJmknt34rfF2K1sVBKlZgaMxVSifTKLyAiosviAiV+crbKimi9EqGaSwegtueG5BuQW5uLYaHDunScnNJGZBU3YP1PJ0DivDj4VcS9Mwfh9W+K8MGRchjQOpfLxVpErxfnPs5EfpMJzSGpsJecQN32Ugz5n98A8kE4UV4PxYEdMElNSKnXonSQHuff+yta7DIMnjANJxpqoVC0jqeZNi4G6U7A+k05Iv9rDKQ6Bd77vBAyvR7N9Y1QhxkhkUrR3CwCF+ZvSRzZ/pTytwy5BU/VPIWf3XM/lO4yqNWDIEiVwO2bAemV7xaK0cRgZfpK33OdSo7X75oEzYVeL/2CFLirmyGPCezKx2umrAno8YmI+hP2uPhJrc2BcE3nb8394bAf4qnpT3W5+3/DnnNICVdj7ojo7wx+9cIQIsd9s1KRU9oIADB9ZxI6d1kVQsLCobJZUKe3IbduGBT6RSgp9CC/qBy/n5mO2KTtiKyQwt4sgSfcjDwANREGlFhtMBqMEL8zlX/LqTroZyei5VQdAKDB5sApbzRyPtzdWo/VishICWIHGwF8e3fR90WEROCF2S9geOw81NZlQqsdAYlEBoSEAoru3VIeqlFAceHSkEQhhSKhc4OliYgoODC4+EmF2Y7EsN6dMbeg2opPciuxYuYgSCUCpNJve1wA4J4ZKVBcCDMXx9wAgCwmCsYQKabEjMDSqMlQCMMgNBTjmMmBfeUObPrfZ+CqWwzBbYZUXo/xESNxeMRE2JQKaFCMhuITEL5395PX7kHz0dZBsONSw3DnLaMRLRUgejywZe5G/KJZvro6Ci4XSaUqjBn9IkakrfPPL4qIiPotBhc/Ka1vRpyxd4PL2h1nEB8aglsnta7LJPlecNGp5PjxlCSIACTf6ckRFDJ4q80QrTrsPjAIZrkexQoZbpo+EoawaCy7/0GoUoYicnQ8To6wwe4sgOzUKUzJSEFM+AncMDMWo8Zc/217cgk8jQ7ormm9PXfKuBiIbi8cUMGWuRu6uXMgCAI8FwbnSjoY4/JdSmUkZLLAXtIhIqKrH8e4+Em1xY5ovX8WnmxP9vkG7DxTjRfuyIBS1jrIU3ZhZlr3dxZ1fGz+cLzbEIVpg8PbvN6QcgoaeyaUsp/j85RC/O6Xv8G5nN2YUvwFys6cRZQyDBmr/olXs7dij3IK4qUFgOckhg77Hxw7tgIa5XOwN7VApQmBbnYSmrKrUAfAc6IOEAC5QoK0n85tc8uz29EaXORK5mMiIvIPBhc/qLE60OT0IErXe9PPv7a7EKmRGiwa8+1twRdnmnW0fDshnEYpw70zUgDtd2oRRaAmD7KRczCuZi+c2mic2v0BJsz7MSZcv6TNcU569PjD0HgsyTLjUYgQIIXb3YTw+GjUnK+ENiwa5poWCIOMiBtsbLPcwPf5Vq9W8a8ZERH5B79R/CCvqnWK+XGJxl5p/1yNDZ+fMuFPS8a06dG4uPqys9nd0UtbeZyAPASoOIq0G+chbfqDHe4qQoBEEOCCAINhPAoL/4yE+Hthr6mCpUGNoRExSEmP6FTdFwPVxcUWiYiIeorfKH5wqtIMtUKK5PDeGaOxcW8RwjUKLPneqtPt9bi0S6YEHBZAFwuMvPxSCiOkFvyhoAILvdVoqYqDzDkdClkGEqdkQCLt2jwkF3tc5OxxISIiP+E3ih8U1zVjUIQGUon/Zzatsznwr+wyPHj9EKjkbYODVC6BRCbAcaUeFwCYdB9Ql986sdtljPOUQghJxrgYBWKHDEeSamy3a7c3uyBXSS+Z6v9q5vV6cfDgQYwYMQJGozHQ5RAR0fcwuPiBucWFUHXnJp7rqncPl0IUgZ9MTb7kZ4IgQK1ToMXaifWRRizu+Gctja2z1YpeXDvqdlwbmtLtets0a3FCre+d30tvKSwsRG1tLb766issXbo00OUQEdH38HYPP6gy2y/pDfGHM1UW/PnTPKQnGDqckVdjVKKp0dH1xr1e4Px+IO9ToPo0MGQuMHwh4KfQAgBNZic0ht4bsNwbTCYTxowZg+PHjwe6FCIiagd7XLrB6xXxdV41qq0O7MitRPb5BhhCLl1Lp6e2HCoFAKxbNqbDfTQGJZrMXQgujSWA6SQAAUicDKjbn47fH5oaHdAYgyu4KBQKOBwOxMZeuqgjEREFHoNLN/x97zn8aceZNtv+cc8kvx9nb0Et7pichCFRHU9brzEoUFFgvnxDLjtw/hvA4wIMCa09K32gyexAVIq+T47lL0OGDME777yDjIyMQJdCRETtYHDpon0FtfjTjjNYMCoGN4yMxi+2HsPrd09ERlKoX4/z2/+cQEG1DStnpV52P02oErYGe/s/rMoFzOWATAEkzwDkvTdB3veJXhFNDQ5oDME1xiUsLAwrVqyAUhlcPUVERAMFg0sXbdpXDAB4cPYQjI43YNmEBL8fo7S+GW/sPw8AmDsy+rL7GiLVcDS7Ybe5oNJeuFxVdw7wfApEjwJiOr7M1JuazA64XV4Yo7q3WGIgqVR9F/CIiKhrGFy64ES5GZl5NfjlguEYHW/olWOIoojH328dGHriqfnQKi9/igxRresjNVY3I0Z7oaYpK3ultq5orG4B8G19RERE/sC7ijop+3wDFr+wF06PF8unXHprcnedr2vCH7efwpenTACAtw6WYF9hHSYkh14xtACAIbI1GJirm/1Wkz+Yq5shCIA+gsGFiIj8hz0unfSXz/IAAB+umuG3O4isdhfu/UcWCmuasGFPEZ6+ZTT+8NEpzBoagRfu6NzgUIVKBo1B4evhuFo0mpqhC1dBKmM2JiIi/+G3SiftP1eHicmhfl2P6Il/n0BhTRM+fmgmIrRK/M+HJ5AQGoJ1y9Jh7MKEdqGxGtSV2/xWlz/UVzQhLLZ3lkAgIqKBi8Glk2YMCYfd7fFrm/mm1sUZR8cbUGtrnYvl6SWjEW/s2uWVyCQdakqtfq2tJ0RRRE2pFRFJHd/GTURE1B0MLp2UHK7BiXKLX9tMCdfg2mGROFH+7TwsUweFd7mdyEQdbPUO2G0uf5bXbU2NTrRYXYhMZHAhIiL/YnDppGhd6y2yVrt/wkG1xY4TFWbIpQL+fGH8zOobhnVrQcLICz0bV0uvy8U6ItnjQkREfsbg0km3TUqEVCLgw5yKHrdld3kw+U87UdbQgi9PV2P32RrfMbrDEBkCuUoKU7F/e4S6q7rYApVGDm0oJ3EjIiL/YnDppBiDCinhahRW93wQ7Gu7zwEAFo6O8W2bOyIK0fruTXwmSATEDjagsqCxx7X5Q0V+I2KHGCAIXe89IiIiuhwGly6QSgSIotijNsobW/Di1wXISDLimR+m+7bfPimpR+3GDTWissAMr8fbo3Z6yu3ywFRkQfww/y6BQEREBDC4dEml2Y7YLt7x810tTg9++veDCFXL8ca9k6FXybF8ShJmDY3AdcMje1Rb3NBQuBwe1JQG9rbo6mILPG4v4oYaA1oHERH1T5yArpNsDjesdjdiDd27nGNzuLHo+T04X9eM9T+dAL2qdRK7Py7xz1pCUck6yBQSlOc1IDqAKzKX5TVCESJDeII2YDUQEVH/xR6XTrp4N5FKLu3yay12F1a+eRjn65rxlx+NxfxRMVd+URdJZRIkpIWhOLfW7213RfHxWiSNDOvW3VFERERXwuDSSTF6FaQSARWNXZta3+3x4kev7Me+wjq8snw8ftgLq0lfNCg9AlWF5oDN59LU6EBNiRUp6REBOT4REfV/DC6dJAgCPF4Rf99T1Kn9RVHEc1+cxZBff4I8kxXrlo7BwjGxvVpj8phwiCJw/kRgel2Kc2shSAQkj+76JHpERESdweDSSa9mFgJovSuoM/59tBz/tzMfALA4PRa3T+7ZXUOdoTEoET1Ij4IjNb1+rPYUHq1B7GADVBr/LEJJRET0fRyc2wk1VgfWfXIGAPCLG4ZdcX+PV8TzO/Mxf1Q0nrttHJSyro+L6a5hk6PxzdYCtFidCNF1fqHGnrI1OFB2uh7XLU/rs2MSEdHAwx6XTrjz9UMAgGXjE/DQnKEd7uf1inB5vPj6TDWK65px/7WDoVbIIO3DgapDJ0UDApB/2NRnxwSAs4eqIJFJMHhCVJ8el4iIBhb2uHSCudmJWUMj8Ndbx3a4z/bjlXjm0zNQK6QI0ygwLtGIjKS+n4QtRKtA8uhwnNlfhfTru7eEQFeJoogzB6qQOjYCyhD+lSIiot7DHpcrsLs8qDDbcfO4+A73OXCuDqvePgKr3YUzVVbsK6zDPTNS+q7I7xk5Mw41JVZUFpqvvLMflOU1oKGyCSNmxPXJ8YiIaOBicLmCg0X1AIDh0R2vdPzm/mIMi9biyJM3YPUNwzB3RBRu7OU7iC4neVQ4jNFq5HxZ0ifHy/miFOHxWiSkcZp/IiLqXT0KLuvWrYMgCHjkkUd82+x2O1atWoXw8HBotVosW7YMJtPlx1vcfffdEAShzWPBggU9Kc1vimpsUEglGB3f/my0BdU2fH7ShB9PToIgCPj5nKH4+12TIJcGLhMKEgHj5ibiXE4NzDXNvXqs+oomlJysQ8YNiVxUkYiIel23v12zsrKwfv16pKent9n+6KOP4qOPPsLWrVuRmZmJiooKLF269IrtLViwAJWVlb7HO++8093S/KrJ6YFaKe3wS3nj3iKEaRT40cS+GU/SWcOnxCBEp0DW9uJePc6hj4ugDVViyMToXj0OERER0M3BuTabDcuXL8eGDRvw9NNP+7abzWZs3LgRb7/9NmbPng0A2LRpE0aMGIEDBw5g6tSpHbapVCoRE+P/qfB7Kt4YgsZmF6x2F3QX1hfak1+DTd8Uo6KxBWeqrPjlguHQKK+uQakyhRSTFw9C5jt5GDsnEZGJHV/q6q6qc2YUHqnGnLtGQCrjVUciIup93fq2WbVqFRYtWoS5c+e22Z6dnQ2Xy9Vme1paGpKSkrB///7Ltrlr1y5ERUVh+PDh+NnPfoa6urrL7u9wOGCxWNo8esOwC2NbcstaB7r+8l/H8NONh3DWZIVMKuDPP0zH/dcM7pVj99TIGbEwRqmx7/0CiKLo17ZFUcS+9wsQHq/FsClXX+AkIqL+qcvdBFu2bMGRI0eQlZV1yc+qqqqgUChgNBrbbI+OjkZVVVWHbS5YsABLly7FoEGDUFhYiCeeeAILFy7E/v37IZW2P3nb2rVr8dRTT3W1/C4bFq1FaoQGf9uZj70FtXjvcBnGJhrx7sqp3VpwsS9JpBLM+OEQbH/pOPIOVCFtmv8GDJ/cU4HKQjN+8PA4LqhIRER9pkvBpbS0FA8//DC++OILqFQqvxVx++23+/48ZswYpKenY/Dgwdi1axfmzJnT7mvWrFmD1atX+55bLBYkJvp/nIlMKsH91w3GL/91HIeK6hGhVeKtFZOv+tByUcqYCAyfGoM9755F/PBQ6MJ6ft7MNc345v0CjJwVh8QRYX6okoiIqHO6dKkoOzsb1dXVGD9+PGQyGWQyGTIzM/H8889DJpMhOjoaTqcTjY2NbV5nMpm6NH4lNTUVERERKCgo6HAfpVIJvV7f5tFbbh4Xh1XXD8Yb907G4f+Z6xvrEixm3ToUcpUMX2w8CY/L26O23E4PPt94CmqdHDOWDfFThURERJ3TpeAyZ84c5ObmIicnx/eYOHEili9f7vuzXC7Hzp07fa/Jy8tDSUkJpk2b1unjlJWVoa6uDrGxgZsL5buUMikem5+Ga4dFBrqUblGq5ViwcjSqz1ux883T3R7vInpFfLnpFOorbJh/32goVFfXgGQiIur/uvTNo9PpMHr06DbbNBoNwsPDfdtXrFiB1atXIywsDHq9Hg899BCmTZvW5o6itLQ0rF27FkuWLIHNZsNTTz2FZcuWISYmBoWFhfjlL3+JIUOGYP78+X54iwQAMakGzL1nJD7bcAJKtQyzbhvWpbEpXo8Xu97OQ2FODRb+9xhEJfdeDxcREVFH/P5f5ueeew4SiQTLli2Dw+HA/Pnz8fLLL7fZJy8vD2Zz6106UqkUx48fxxtvvIHGxkbExcVh3rx5+MMf/gClUunv8ga0IROi4Ggejsy389DU6MDce0Z2qtfE0eLG538/ibLT9Zhz1wikjgvOniciIgp+gujv+2QDxGKxwGAwwGw29+p4l/6gOLcWn/39JJQhMsy6dShSMyLbnWBP9IooyK7G3q35cDk9WLByNJJGhgegYiIi6q+6+v3N4DJAWWpbsOe9fBQfr4U+QoWhE6MRHq9FiF6BZosDdWVNyM8ywVpvR2pGJGb+aKhf7kgiIiL6rq5+f3N05QCljwjBogfSUZHfiDMHKnFiTzkcTW7fz1VaOVLHRSJtWixiBxsCWCkREdG32ONCPo4WN1osTqj1CihCmGmJiKj3sceFuk0ZIoOSgYWIiK5iXBmPiIiIggaDCxEREQUNBhciIiIKGgwuREREFDQYXIiIiChoMLgQERFR0GBwISIioqDB4EJERERBg8GFiIiIggaDCxEREQUNBhciIiIKGgwuREREFDQYXIiIiCho9JulgEVRBNC6PDYREREFh4vf2xe/x6+k3wQXq9UKAEhMTAxwJURERNRVVqsVBoPhivsJYmcjzlXO6/WioqICOp0OgiAEupyrlsViQWJiIkpLS6HX6wNdzoDH83H14Tm5+vCcXF38fT5EUYTVakVcXBwkkiuPYOk3PS4SiQQJCQmBLiNo6PV6/gNwFeH5uPrwnFx9eE6uLv48H53pabmIg3OJiIgoaDC4EBERUdBgcBlglEolfvvb30KpVAa6FALPx9WI5+Tqw3NydQn0+eg3g3OJiIio/2OPCxEREQUNBhciIiIKGgwuREREFDQYXIiIiChoMLj0I7t27YIgCO0+srKyLtm/oKAAOp0ORqPxim2XlJRg0aJFUKvViIqKwmOPPQa3290L76J/6cw5ycvLw/XXX4/o6GioVCqkpqbif/7nf+ByuS7bdnttbtmypS/eVtDqzfPBz0j3dOac7Nq1CzfffDNiY2Oh0Wgwbtw4bN68+Ypt8zPSPb15TvzxOek3M+cSMH36dFRWVrbZ9uSTT2Lnzp2YOHFim+0ulwt33HEHZs2ahX379l22XY/Hg0WLFiEmJgb79u1DZWUl7rzzTsjlcvzpT3/y+/voTzpzTuRyOe68806MHz8eRqMRx44dw3333Qev13vF3++mTZuwYMEC3/POhNCBrLfOBz8j3deZc7Jv3z6kp6fj8ccfR3R0ND7++GPceeedMBgMWLx48WXb52ek63rrnPjtcyJSv+V0OsXIyEjx97///SU/++Uvfyn+5Cc/ETdt2iQaDIbLtrNjxw5RIpGIVVVVvm2vvPKKqNfrRYfD4e+y+7XLnZPvevTRR8WZM2dedh8A4r///W8/Vjfw+Ot88DPiP509JzfeeKN4zz33XHYffkb8w1/nxF+fE14q6se2bduGuro63HPPPW22f/XVV9i6dSteeumlTrWzf/9+jBkzBtHR0b5t8+fPh8ViwcmTJ/1ac3/X0Tn5roKCAnz66ae49tprr9jeqlWrEBERgcmTJ+P111/v9LLw1Mpf54OfEf/pzDkBALPZjLCwsCu2x89Iz/nrnPjrc8JLRf3Yxo0bMX/+/DaLT9bV1eHuu+/GW2+91enFsaqqqtr8RQPge15VVeW/ggeA9s7JRdOnT8eRI0fgcDiwcuVK/P73v79sW7///e8xe/ZsqNVqfP7553jggQdgs9nw85//vLfK73f8dT74GfGfy52Ti9577z1kZWVh/fr1l22LnxH/8Nc58dvnpNN9MxQwjz/+uAjgso/Tp0+3eU1paakokUjEf/3rX222L1myRHz88cd9zztzqei+++4T582b12ZbU1OTCEDcsWNHz95ckPLnObmopKREPHnypPj222+L8fHx4jPPPNOlmp588kkxISGh2+8pmAX6fPAzcqneOCeiKIpfffWVqFarxTfeeKPLNQ3kz4goBv6c+Otzwin/g0BNTQ3q6uouu09qaioUCoXv+R/+8Ae88MILKC8vh1wu9203Go2w2Wy+56Iowuv1QiqV4rXXXsO99957Sdu/+c1vsG3bNuTk5Pi2FRUVITU1FUeOHEFGRkYP3l1w8uc5ac9bb72FlStXwmq1QiqVdqqm7du3Y/HixbDb7QNuTZdAnw9+Ri7VG+ckMzMTixYtwrPPPouVK1d2uaaB/BkBAn9O/PY56XTEoaDh9XrFQYMGib/4xS8u+dmpU6fE3Nxc3+Ppp58WdTqdmJubK9bX17fb3sUBVSaTybdt/fr1ol6vF+12e6+9j/7kcuekPW+88YYok8lEp9PZ6WM8/fTTYmhoaHdLHFD8fT74Gem5K52Tr7/+WtRoNOKLL77Y7WPwM9I1/j4n/vqcMLj0Q19++WW7XX7tae9S0QcffCAOHz7c99ztdoujR48W582bJ+bk5IiffvqpGBkZKa5Zs8bfpfdblzsnb731lvjuu++Kp06dEgsLC8V3331XjIuLE5cvX+7b5/vnZNu2beKGDRvE3NxcMT8/X3z55ZdFtVot/uY3v+mT9xPs/H0++Bnpucudk4uXItasWSNWVlb6HnV1db59+BnxP3+fE399Thhc+qE77rhDnD59eqf2bS+4bNq0Sfx+Z1xxcbG4cOFCMSQkRIyIiBB/8YtfiC6Xy18l93uXOydbtmwRx48fL2q1WlGj0YgjR44U//SnP4ktLS2+fb5/Tj755BNx3LhxvteMHTtWfPXVV0WPx9Pr76U/8Pf5EEV+Rnrqcufkrrvuanc8xrXXXuvbh58R//P3ORFF/3xOOMaFiIiIggbncSEiIqKgweBCREREQYPBhYiIiIIGgwsREREFDQYXIiIiChoMLkRERBQ0GFyIiIgoaDC4EBERUdBgcCEiIqKgweBCREREQYPBhYiIiIIGgwsREREFjf8PwgZ5demNEpkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 12068 in-HD and enclave pops are 404168.0 495747.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 13002 in-HD and enclave pops are 680371.0 495747.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in enclaveOnly:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    ePop = np.sum([unitPop[u] for u in enclaveList])\n",
    "    print(\"for t\",t,\"in-HD and enclave pops are\", HDvPop[t],ePop)\n",
    "    for u in enclaveList:\n",
    "        plotPoly(unitGeom[u])\n",
    "    for uu in HDunitList[t]:\n",
    "        plotPoly(unitGeom[uu],0.2)\n",
    "    for u in borderSet:\n",
    "        plotPoly(unitGeom[u].centroid.buffer(0.004),0.5)\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6a6bc47a-5b0a-472a-9d0e-34cd81bec542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Special for NYlower, we will tack on Staten to [12050, 12068, 13002] and then MUSTFREE 3625\n",
      "adding neighbor 7192 w pop 495747.0 to HD 12050 w current pop 647331.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if OK 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding neighbor 7192 w pop 495747.0 to HD 12068 w current pop 404168.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if OK 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding neighbor 7192 w pop 495747.0 to HD 13002 w current pop 680371.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if OK 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Special for NYlower, we will tack on Staten to [12050, 12068, 13002] and then MUSTFREE 3625\")\n",
    "unitToAdd = 7192\n",
    "for t in [12050,12068, 13002]:\n",
    "    if unitToAdd not in HDunitList[t] and len(set(unitNbrs[unitToAdd]).intersection(set(HDunitList[t])) ) > 0:\n",
    "        print(\"adding neighbor\",unitToAdd,\"w pop\",unitPop[unitToAdd],\"to HD\",t,\"w current pop\",HDvPop[t])\n",
    "        isOK = int(input(\"enter 1 if OK\"))\n",
    "        if isOK == 1:\n",
    "            HDunitList[t].append(unitToAdd)\n",
    "            HDvPop[t] += unitPop[unitToAdd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "17f146cf-fb54-4fca-b0a3-85c4325693e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 4 HDs\n",
      "working on HD 3625\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.99987 0.07346\n",
      "CCB cluster 0 = unit 7192 with pop 495747.0 now has use of 0.9311182009021701\n",
      "CCB cluster 1 = unit 7193 with pop 52630.0 now has use of 0.9960190311906133\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate([3625]):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d25f9154-bb61-4342-89e2-bfe84485d4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final ??? check -- any more disco's ?\n",
      "working on HD 1444 time is now 0 sec\n",
      "working on HD 2999 time is now 7 sec\n",
      "working on HD 4146 time is now 14 sec\n",
      "working on HD 5924 time is now 23 sec\n",
      "working on HD 6482 time is now 30 sec\n",
      "working on HD 7033 time is now 35 sec\n",
      "working on HD 7625 time is now 43 sec\n",
      "working on HD 8140 time is now 50 sec\n",
      "working on HD 10265 time is now 57 sec\n",
      "working on HD 10785 time is now 64 sec\n",
      "working on HD 11301 time is now 72 sec\n",
      "working on HD 11813 time is now 78 sec\n",
      "working on HD 12320 time is now 91 sec\n",
      "working on HD 12837 time is now 97 sec\n",
      "working on HD 13604 time is now 109 sec\n",
      "out of 7263 total HDs, there were 7260 0 3 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 113 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "#check contig one more time after triaging these 4 HDs\n",
    "print(\"final ??? check -- any more disco's ?\")  #run again if we ran above block to fill minor enclaves\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2e92105e-83c8-446b-9a36-90a67f8a2838",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 12050 in-HD and enclave pops are 647331.0 1250\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 12068 in-HD and enclave pops are 404168.0 1427\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for t 13002 in-HD and enclave pops are 680371.0 1396\n"
     ]
    },
    {
     "data": {
      "image/png": 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5edPlli5Ti4eO+M0UBEG4CJLVSvnDjyA7nUS+8g9UJq+hbpJwGehiY4n4618ovf8Bqp99ltBf/3pIeveam/cDMiHBN/RxVqa+Po+CunRK/YJRAHqlgtHeRszqazN9ggh6BEEQLpAsSVT98EfYT5wg+j//QRMaOtRNEi4j44QJhP7qOap/9Aza6BgCvv3gZbu3w9FEc/NefHwnodMG9FnGzy+Rrq5GtKE+zPPvf5cBs1pFm8v9tQ6IRNAjCIJwgepffpn2jRuJ+OtfMIxKHurmCEPA56abcJaVUf/SS2gjIzAvWXLJ79nScghJchAUdN05e5ciIsYBUFK6CZvLhl597hVcMQYdRV12UszGQW3vlUQEPYIgCBegedUqGl97naAf/RDvefOGujnCEAp49FEcZeVU/fBHaEJDMaSkXJL72DubaGzejH/gTHS64HOWdbqcZDUcpdlai83VQdcAgh5FZwdlx08w3O2Z+CxLEt6zZw9W868IIugRBEE4T51791Lz7HP43HE7ft8QS9OvdQqFgtDf/BpnVRXlDz9CzAfvo42IGNR72MvacHe6kLtU6CLOHfCsL/wCF0rGBo0hLWRcv3Wv2bYbhUKBr0HPgtRkDEZPT0/Hzp2D0vYriVi9JQiCcB6cVVVUPP4EXlOmEPKTn4il6QJwMmvz3/6K0mSi4pHvIg3Sii7J7qbraD0qsxbjiCAUKg1ux7mXoMf5JRDpHUaEd/iA7qFUwPWzpjJtYlp3wAMgu91Yjx6lfds2HBUVF/UcVwoR9AiCIAyQLElU/egZlF5ehP/h92JputCD2teXiL/8GUdREfV//NNF1+eoaMde3IphVABqH8/QVPDI+dQe33TO6+J946loyTuPO/UduBtTU9FERGCaOZOOrdvOo74rlwh6BEEQBqjprX/RdfAgYc8/j8piGermCFcgfWIigU88QdNbb9G5b/8F1SF1Oek6Wo9Sr8aQ5IdCeTooUWkNyJILt8vZ57WF9fvZdOxF6my17K8vGvA9ZVnudUxlsaDy9qbtiy/wWbni/B/kCiSCHkEQhAGw5eZS/8c/4nfffXhNnjTUzRGuYH7fvA/jhAlU/ehHF5SxWXbLKI1q1AGGPs8HJs2jPmdzj2OSJLE972+0WauYn/wD7hvzKDtqsgfWXos3DXX1vdshy7St34B50SKUxq/Hii4R9AiCIPRDstupevpptLGxBD75xFA3R7jCKZRKwl54Hqmzk5rnfnXe16u8tch2d5+9LwBaoxm3owtJ8mRQLm86ysbjzzMmcjmpUcu7y8VbwslsKuv3fiPjojle0nsfsfYNGzHNno1Cqz3vZ7hSiQFpQRCEftS/9DKOklJiPvwQ5dfoDUC4dDRhYYT8/OdUPf00pjlzsFy/9Lyu10aacZS3o4vqO6mg/7Dp1Odu54SmAK3KyKJRP+lV5qaocfwh+3NS/KL6rCMrv5Cy6lrcksSYuOge5xo2b8KSmvK1yzAuenoEQRDOoXPvXprefpvAp55Cn5gw1M0RriKWG67HfN111Dz7LM6qqvO6VmXWIrV7VoBJkoTkdvc43+6UyK/JJilkARPj7jprPaEGXwrb6nocc7lcfLB5J1qnk6WzprJsznRioiO7z5dv3cyJkgI0QUHn1eargUI+W//ZNaitrQ2LxUJraytmc/8puwVB+Hpzt7RQdONNaGNjiXrzDRRK8TlROD/u1lbPz1B0NFFvvXleP0OuBiu2hnra63JQqFWETJ2PJEns3r0Js8HC2PH9zy1759g7ZFtVhHhHMz84FnMH7D6Wy7LpE/Hy6t2L05yfx7G1axh79314+/e9vcWVaKDv3+I3WBAEoQ+yLFP97LNIVithLzwvAh7hgqgsFsJeeJ6u/ftp+tfb53VtuyoLGxWEzlyEUq+jpqSW7WtyyHJ39BvwlLeX88L+F5gQOoEXxt/BY/GTKepoYtva97lj0Zw+Ax5HRztlmzcSPmsOJj//82rr1UL8FguCIPShbc0a2teuI+QXP0cTEjLUzRGuYl6TJ+N3333Uv/wytrz+8+fYOjvYs+Z3GI0xBCRNBWCP003JiXLm3JhMRHA4n+bs4Mc7f0xdV12v6z/I+4D1xev5wYQfkOiXCIBSqWRZRDIjQs20t/ZeqSVJEtlvvcGo+7+F0WzB2nb+q86uBiLoEQRB+ApnZSU1z/0K8w03YFl6fhNQBaEvgU8+gTY2lqrvP41kt5+1XFV+LjUF+SSMX0ZF7iEAVu1Zx9iY4UxeMB4Au1PJsbpSfj7l53yQ90H3tbWdtfzuwO9I8kviW2O+hbKP3snUhXdxbOMHvY4ff+t1Em++FZVOj29YOE1VX48MzF8lgh5BEIQzyLJM9c9+jtLbm5Cf/XSomyN8TSh1OsJ+/3scJSU0/O3vfZbJXP8FJl8/YsamERA6gqZj22hedz9RPl7Isov6jjZ+u3ktsX7+/HjWPejVepQKJV2uLj4t+JSPTnzE98Z/jzGBY87aDrVWhyS5cTtPb5OR/8H/CJ00Ga/QMAC0egPOrwRmTruNipxsSo5kDMJ3Y+iIoEcQhKtedXX1WXOanK+OLVvo3LOHkJ//DJVY0CAMIn1iAv4PPkjTW2/hKOudP8fkH4A5MAh7UzV57/+BpKn3Ypn7R5JsB1mVvpt3Dx/kB7MXMj4irvuau0fezXN7nyPcFM7DKQ+jVvafiSZ54W1krv8vAMVrv8Q7PBL/Ub0DJafNRmH6AYoPH6KmIJ+gmLhB+z0bKiJPjyAIVzW3201DQwMNDQ0kJyf32aU/UJLDQe3vXsRr+nRMs2cPXiMF4ST/bz1Ay+rV1L74IpF//WuPc35h4eR8+jp6NSTc8hQKpRKbtZmymmOsnDCF4UFTetVn1pp5YcYL59UGi08wtvZmOltbqM89xsQnn+5VxmG1UpGTTWzqOJRKVfdxrcGIvasTnfHqzN8jgh5BEK5qpaWlJCQkoFQqyc7OZsSIEWg0mguqq/nf/8ZZWUnk3/8mdk8XLgml0UjQ975H1dNP07lvH16TJ3ef8wuLwBikRD/lfgBqNv2HEms2E5b8A5V6cJNijrnuXo6uexuTxafP84lTpvc65nI4aKmpwhIUfNUGPWJ4SxCEq5rT6USn06HRaBg1ahS5ublYrdbzrsdVX0/DP17B94470A0ffglaKgge5uuXYkhJofa3zyO7XD3O6aPTkGryaHvreXx8wnB2RA16wAPgbQnANyQGOcCLmj27BnSNSq3Gy+KDyddv0NtzuYigRxCEq1Z7e3uPfCNKpZJRo0ZRUlJCe3v7edVV98c/olCrCfzuI4PdTEHoQaFQEPyTH2PPz6flww97nLM2aehY9wnedzyOfvw86sqCaO/quCTtSJy5jE57E40n+l9GD549xU7N6GlrqKckM53y41mXpG2Xigh6BEG4alVVVREeHt7jmEKhYMSIEdTW1tLU1DSgeqzZx2hd/TEBjz2KysfnErRUEHoyjB6N5aabqP/jn3C3tiK7JJpXbQK3C/N9P0ShN9JRUU/qmGi27Tt4SdrgGcKV0Vl8yHrtFUrWftH/NUolxYcP4ejqJHpMKm6Ho99rriQXFfS88MILKBQKnnjiie5jNpuNRx55BH9/f0wmEytXrqS2trbfunJycli2bBkWiwUvLy8mTJhA2Rmz22fPno1Coejxeuihh3rUUVZWxtKlSzEajQQFBfH000/j+krXoSAIXw+nVpGcbe7N8OHDaW9v7/fvjyzL1P72t+iGD8P3ttsGvZ2CcDaBTz2J7HBQ+4c/Y8ttQj8iDleTs/t85e4couemolKp6OjqvCRtGL3wDhqkOkY/+BCddf2/V8eMSSU2dTwBUTHdPT9X04quC57IfPDgQV599VXGjOm5zO3JJ5/kiy++YNWqVVgsFr773e+yYsUKdu/efda6CgsLmT59Og888ADPPvssZrOZY8eOodfre5R78MEHee6557q/NhqN3f/vdrtZunQpISEh7Nmzh+rqau699140Gg2//e1vL/QxBUH4ClmW2Vu9d1D/0MknO80VDHzycG1DLQviFpyzTHR0NJWVlVRUVBAREdFnmfa1a7FmZBD5xuso1GJth3D5qAMCMS+/h5b3Xsf/m3ehi4uj8a2jwHhqD+ah1ChR6TTMmzqJDTv3csPc2YPeBpMlAK3Bi/rKAsCzFYXW5N2jjKO9nfyP3idh5W1ovXue8/Lxpau1BS8f30Fv2yUhX4D29nY5Pj5e3rhxozxr1iz58ccfl2VZlltaWmSNRiOvWrWqu2xOTo4MyHv37j1rfbfddpt89913n/OeZ96nL19++aWsVCrlmpqa7mP/+Mc/ZLPZLNvt9gE9V2trqwzIra2tAyovCNcam8smf174udxubx/qpsgfHPhgwGVra2vloqKiXsfdXV1y/pw5ctn/e3gwmyYI/ZIkSW7fVSG72rvkE/Pmy6UPPijLsiy378uS97+wWi7fmtmj/Gebt8oOp/OStMXtdss7/v072d7RIR9945/ysf/+W+5qaZZlWZbzP/ifnPnq3+WCTz6Sq/fv63Wtw2aVy44dvSTtOh8Dff++oOGtRx55hKVLlzJ//vwex9PT03E6nT2OJyUlERUVxd69e/usS5IkvvjiCxISEli0aBFBQUFMmjSJTz75pFfZ//73vwQEBDBq1CieeeYZurq6us/t3buX0aNHExwc3H1s0aJFtLW1cezYsT7vbbfbaWtr6/ESBKFvDdYGtpZvZXHMYkxa01A357wEBQXh4+NDfn5+j+ONb76Jq76B4B/0zlMiCJeSraAFw9hAVCYDQT94ms4dO2lcs5GSci3WyBFEzB7bo/zMiePYemD/JWmLUqkkMmUapfkHGH3/gyQsXUbl+vUce/tN/MekYImORm30ImRi701ONTo9rqtoXs95Bz3vvfceGRkZPP/8873O1dTUoNVq8fnKRMDg4GBqamr6rK+uro6Ojg5eeOEFFi9ezIYNG1i+fDkrVqxg+/bt3eXuvPNO3nnnHbZu3cozzzzDf/7zH+6+++4e9z4z4Dl131Pn+vL8889jsVi6X5GRkQP6HgjCtaaopYi8pjwWxyxGdUaisquJr68vYWFhHD9+HFmWcdbU0Pj6G/jdcw/amJihbp5wDWmpqSZv2zZUJs9SdO8FC9COHUft715kxA3RxE8M4diuyh7XWEze2Kxn37PrYsWMnkZjoaeDQG2xMPzW20j+xv34JSbRUVlF9IJF3WUlt5uagnyKDx+i+PAhzIFBl6xdg+28BrDLy8t5/PHH2bhxY6/5NhdKkiQAbrzxRp588kkAUlJS2LNnD6+88gqzZs0C4Nvf/nb3NaNHjyY0NJR58+ZRWFjIsGHDLujezzzzDE899VT3121tbSLwEYSvOFx3GI1Sw7TwaUPdlItmMpmIi4sjOzsb3//8B6XRSMD/e6j/CwVhkDhtNupKioiePZ6aAzmETByBQqGgY9JdaF//Pq0frCLsnrupKWylubYT32BPSga3y42qUcuOozuZOWbGpWlcH4sCKnduR+Nl7HEsZ9c2YlPGETI8oc9qJLeb+tJiulpbUGu1RCaffS+wy+28enrS09Opq6sjLS0NtVqNWq1m+/bt/PnPf0atVhMcHIzD4aClpaXHdbW1tYSEhPRZZ0BAAGq1mpEjR/Y4PmLEiB6rt75q0iRPN1tBgWfyVUhISK9VGqe+Ptu9dTodZrO5x0sQBA9ZltlWvo0AfQCjAkYNdXN6kS9wIrVer2eYl4m2NZ/j++C3UH1lYqYgXCqy00nb55+jyD6Gb3wknRVN3R/8vccmY1ywhMbXX0dyOEhZEMmRdaVIkoTT4WLrJ+nMWpxGTWv1JWuf1uiNtev0NA9HezvNhSdIvO3OHuWMFh+MJzM5y7JMR1Mj5cezunt+yrKPYPLzJzZ1PG6nkyvJefX0zJs3j6ysnomIvvnNb5KUlMQPf/hDIiMj0Wg0bN68mZUrVwKQl5dHWVkZU6b03jMEQKvVMmHCBPLyeiZHys/PJzo6+qxtyczMBCA0NBSAKVOm8Jvf/Ia6ujqCgjxdbRs3bsRsNvcKqATh60qWZQ7WHMTutqNUXFwaLrvbzrjgcVh0lkFq3ZWj9d9vo/b1pXb0aLy6unqsBBWES0F2uWhbtx6/Zcuw79xO7d7dhMwYSfnmDKIXjCduXhTZ+deh2vAlbZ99hnPEHMIC9eSvK6Giq4EZS1PQGbSMjBrJwRMHmRA/YdDb6B+dSG3xMWKSPe/XJevXEn/jil7l3E4nxYcPeVZdymDy8ydkWDwaXe8RoCtuMfvFzpj+6qqqhx56SI6KipK3bNkiHzp0SJ4yZYo8ZcqUHtckJibKq1ev7v569erVskajkf/5z3/KJ06ckP/yl7/IKpVK3rlzpyzLslxQUCA/99xz8qFDh+Ti4mL5008/lePi4uSZM2d21+FyueRRo0bJCxculDMzM+V169bJgYGB8jPPPDPgZxGrt4SrWX5TvrypZJPcYmsZ6qZcFu/vf/+CrnPW1ck5o8fI9f94RZYkSc7JyRG/88IlJblccstnn8nuM1YSZ7z8W7mjs00uWX9Q7qhrkmVZlq0tVvn4zffLeXMWyEVbSmRZluX0dRly5fHSHvW9v+PCfvb7Y+tqlw998a/ur4+9/Waf5dxulyxJ0oDqLDlyWHZdolVnZ7qkq7fO5eWXX+b6669n5cqVzJw5k5CQEFavXt2jTF5eHq2trd1fL1++nFdeeYUXX3yR0aNH8/rrr/PRRx8xfbpnwzOtVsumTZtYuHAhSUlJfO9732PlypWsWbOmuw6VSsXnn3+OSqViypQp3H333dx777098voIwtdRg7WBjaUb0Sg1zIue97XsmRlMTf/+NwqNBt8770ChUJCUlER9fT2NjY1D3TTha8jldlH73//gPX8+Sq1n4nJtaQ6doQY+3P0vIuenUbMjGwC9RY/59ntxV5Xjb88FIG1RKrWlVbgcp4eJ4kLjyC7NHvS26gwm3A5bv+WUStWAN+S1BIfQOoCkh5eLQpavolSKl1hbWxsWi4XW1lYxv0e44jncDnZX7sais5AWnDbUzbns3j/wPrdNPL8Myu62NgrmzMX3jtsJ+v73e5wrKytDq9WedQ6gIJwvSZb4suhL7EePsnL5jwGoq8inYM86pt76GMeK0mlorWWMaQy2hlZCpyQDUHrfN5Ha24n5cBUKhQKnzcGx7YdJWXR6yfh729/j9lm3D3qbD3zyKhNv+o6nHWs/xysqmoDk0RdcnyS5Kcs+SsyY1MFqYp8G+v4t9t4SrgoOt4OcxpyhbsYVQZZlDtUcYl/1PqaHT78mA54L1fy/95CdTnzvvbfXuaioKCRJory8fAhaJnzdSLLE/3L/x7zoeUybsJIdW/5NaelRTuz6gqm3PgZActw4GjvqsfnK2BracHRaAfB/8FvYjh2jc88eADR6LSFx4ZQdKeyuf3zCeDYf2XxJnyF6yfVU7NiGdIHbObldLpw2Gy77pVtqf75E0CNcsWRZxul20uXsYkvZFsw6MxtLN3afd7qdfFLwCTsqdlDRXjGELb18ilqL2Fy2mTifOGZGzESj0gx1k4bMQLvXT5FsNpr+/W8sy5ejCeo7r0hYWBh6vZ6ioqLBaKJwDcttymVK2BQMagMhEYkoVSoObH+fabc/2aPckkm383+b3iNy4XjK16cD4DV1KvrkZBr/+Vp3uZD4CNqaWulq8ey4Pjx0OPUt9YPebvkrU4+Tbr2TzFf+es7AR3K5KP5iDTnv/Jvs9/9LYfoBSjLTqcjJpqG8jJiUcYPezgslNpoRrjj5zfn46nzZWLqRYT7DUCvVjPAfQbgpnOLW4u5yNZ01pAalEm2OZk/VHhQKBeGm8HPUfJokSzjcDvTqwck3dam12Fo4WHuQWHMs86Pn93/BNeB89ukCaFm9GndzM/4P3H/OcoGBgTidTg4fPkxq6qXtkhe+vkb6j2R9yXqivaPpaCvH4OOiyGDGJblQK9UUNtYQbvbjxV0f8NCMu/hk6yHmjoyi5mAuIROS8H/wQSqfeALrkSMYxnqyM4+clcLhtfsYt3QqbsmNSnUJEoV+ZcKL3t+f5LvuJftfr6PW6kCpRKFUIjkdKFSeEEKBgohZs/GOjKJj+3aMo1O65y9daUTQI1xR2hxt1HXVUdJaQrgpnEmhPdOen3qjy2vKI702nZsTbgZgathUtpdvH3DQU9lRSXptOv56/+5jGpWG4T7DCTAEDNLT9NZqb+VE8wlGBYwaUMDllJzsrdqLQW1gftT88+7dEDxkl4umN97EvGQJ2qiofss3NTUhyzJHjhxh7Nix/ZYXhB5OTpWdFTGLL/M/RN9QSIhvPEtMY/nHkX8Qph1HTn0J7fZOfjLrbkxaHX85WE6SSYkxvxmn1Y73gvloY2JoeO01Iv/6V8CzXcTwcUkc35ZJpU89M0fNvCTNlyQJpfL0QJDO148x33oIl8OO3NqGo7wcufMru76XlNJRUobCYEChvHIHkUTQI1wxWu2t7K7czZLYJcjItDvae5XpdHZS1FqEzW1jSewStKrTnyaMGiN7qvYwNWxqv/cyaUwM9xneI+mew+2gqLWI3KbcHmVDjCHEWGJQK8//18XqspLdkI3V5RmrN2vNjPAfQWZ9JnaXHaPGSGpQap91Z9Zl0mxrZmr4VHQq3XnfWzitbe1anJWVRPztr/2Wra2tJTg4mMDAQFwXOJdBuIZJbjj6PmhN6ONmMb3iOD4hKSiSl7Nh569pdpsJ8pP56WxPwj9JkvjlrkJ+vCCFTzMzuGd+CmXrDjFs+TT8v/UA1T/9GfbCQnQndx6whPjhcjgpOlhIcErwuVpyQRRmLU3VJwgIT6SzOp+2o9uRHSfn5CgUYNERNGolKh+fq/JDmAh6hCuCU3KyrXwby4YtQ6FQoEDR59LrKWFTKGwpJCUopde5CSET2F25G/As4z6zx6bF1oJBY+gOHiw6S6+J0VqVliS/pB7HZFmmprOGAzUHkGSp+7hRbSTeNx5vbc9svi7JxfHG47TaPSkZ9Go9yf7JGDU9k99NDp0MQIejg33V+3BLbnz1vowKGEVleyV5zXmMCRzT53MK50eWJBr/+Rpes2aiT0o6Z1m3201TUxMjRowAQK0WfyKF/smyzOHyFtKifEGWqWu3ETRiIpzYiO+8X8LW30DHdGZPfJz29a+wcrZnSxdJkvjBjhN8b3wMwV467hqfxrvpmSwbHkLNwVyCly2j/i9/pfG11wl74fR+l3o/I5FRA+vVPh8HSj+lxdxE19b3qPTxRxcYSdCsO1HpvbrLdHTkY1M0YFL4Dvr9LwfxGy0MudUnVrM4ZjG+et9+Pzl4a73PGQi4ZXf30FWsORatStsd4LQ52rhx+I0AKBVKJKSz1nOKQqEg1BRKqCm0x/FOZycnmk/Q4fRMKjyV+UGlVDHSbyQ+gT791g1g0pqYHu7JR9VobWRX5S6CjcFi3s4g6ti2HfuJE4T84uf9li0oKGD48OGXoVXC10l6aTM2p8TRihYcLgnZMpGGggJGTvYMvzPrR7Dhp2iXvMCEpAUUHNlF3Oip/OLjY9wzLYpQk+fDmJdOR0pYMMdsDqLLWrB12PD75n3U/eH/CHzsUTRhYQDkZR1lVNr4QWu/JEl8nvNXhvmNYeKE78M5kj2bTAnU1a3HyxiHQnH1bT585Q68CdeEQzWHmBE+gx0VO4AL30/plJH+IzlQfYBlw5ahVCjJb86n0dqI1WUlzhLHgeoDbC7bTKezE5d04UMXXhovUoJSmB4+nenh05kRMYMZETOYGjYVH73PBdXpb/BnZsRMEv0SL7hd15KB/qw0vvYahrQ0jOPP/Sbhdrtxu91oNNfuijjhwoyL9qW5y0GHzYVRq2bCmFHIkkRDVamnQO7nEOwZSo9KSKGzoZLX3zvIQ9Ni2Vvc3LOumBjKW9vwnpFM5abDWFauROXlReNb/+ou43a50OoGZ8i7xVrDe0d+w4zYW0gOnT2ga/z9Z9LYuGNQ7n+5iaBHGFKdzk4CjYFMCp3E0fqj3XNfLlSAIYDl8csBSA5I5vq460kOSOa6uOsYHTiaEf4jmBM5h92Vu1EqlNR3Df6ST+HKYcvJwXr4MP73f7PfsiqVCoPB0CNbvCAMhEKhwNeoZXiQiZFhnsR4yVOvo+TwJqRjn4CtFdLu7i4/vC6ftOYSwkNMXJcQxMdHqnrUd+f4VFYd2E3orNFU7snF547baV29GslqxWrtQq0dnMD8eM0Othe9z+1jf4KvMbT/C05SqQxoNL7YbFX9F77CiKBHGBKSLLGlbAvxvvEA+Op9uWfkPb3mvlysrw6XeWu9USqULIxZiNVl5Xjj8UG9n3BlaVn9MaqAAEyzZw+ovEqlEvN4hPOWXdmKl05FkLnniszawOlkHtgB477Rfcz6/m/QzbidhKUzKfh4D1H+RvQaFXk1pxduHC3OZIJFounYOhQqFdqZc5E6O2nfuJHco5kkjUm56DZvOvEW7fYmbkx+vMdKrYGyWFJobTty0b3zl5sIeoTLrsnWxIaSDUwOnUyYKaz7+OXeM8pL44VKefWNSQsDIzkctK1Zg2XZMhQDDGSsVitt7Rvp7Cy4xK0Tvk5yatsI9jX0OLYlt5ZGtw7LuJspzc0AoGvVC2im3ow6Ih5zTChuh4uOinqWjAxme0kTTrdEZmEGapWK8ROuQ6FU4jNcS0PGcQwTJtDy0WpcThcGw4V/OLQ7O3k/83mSAicyKfqmi3lsfH0m0dyy76LquNxE0CNcVg63gwM1B1gcu3jQe3XOV1XH1dc1Kwxcx5atuFta8FmxfEDl7XY7arUNoyEGUFBfv+mStk/4erBLEopQI3/PKMPl9iyO2JRXy7HKVu6dEsOwMVNpLD1G67u/QjVyHurI03P2upo6MIZ5coXdMTaMl3ccBoWC0bEpAETMvp3qtf8gZHoCxmFxdO3fj+oiNsYta87m0+N/4aZRjxPhk3zhD32SVusHsoTTefUMCYugR7isDtYcZG7k3KFuBgDXxV7HhJBzLFMQrmotH69GP3YMugGuxio5XoBOmYfZPBovr2FX5coU4fKSJCev5m5kSYCJx8ZH89LBEnaUN/NlVTPDh/l1lxsz9za22+LRJXv+3rS11LHpT0/jGx9I3aF8AGyZu7jX6CIl7ows4Nt+R+KyBzElpeF7792g0xGYd+KC2rq/9GMKGtK5deyP0KkH7wOnr+9Umpr3DFp9l5oIeoTLRpZlHG5Hj4SCQ8mkNYmkf19Tzto6OnfuwmfFygFf03piH0HBc7uDHYVCRUPDVtzuK2ezROHKIUku6uq+5MH46XxaupcgLx2Tw33Ja+rgpTlJHGnqpMnqBECt0ZI8eSJ7Nx+gpbmJHe/9kZnffpaK2u005lRQs3cP7roiQiadkYF+24sQvxDCUwDQxA3HMToe29p1yFL/6TZOcUsuPsl+GYs+kLnx3+j/gvOkUCgwe4+mre3ooNd9KYigR7hsMuszRbI94bJo/fRTFFot5uuWDKi82+4CRRd2qYHmloPIsoy//0z8/KZRU/MxkiQCH+E0T8DzBUFBSzBovBjrF8EHhTvI6zzEA6M9SQOfGhvJi4ePdF8zbGQcDc2d7Hh7DVmJo9EajESmzaSuayeHj6wnxKscupo8hXf8HwyfA+E9934L+84DuKqr6do3sHk0TZ2VvJ/5PHOG3UlS8PTBefg+GAwR2B31V8UHBBH0CJdNi60FX/3VmcVTuHrIskzr6tV4L1yAytu7/wuA4oPHCYoOw2Ytw+Q1nIaGTTQ376OzqwiFQo1SKXoEBQ9Jkjh8+I8EBS1GqfT0Wif7DSfUaOb6yLGsLd8PwPqK/SwOdrM12zP009rlYIvbl8XfvYclSVP435HtRI+YSOot32bMDUs5EhgHO/8Pdv0RoiZBRO+h94Dpi3CH+tCw6r1+25ldvZXdJR9xe8pPsBgGf7uKr/L3m0Vj07ZLfp+LJYIe4bIobi0mxhIz1M0QrgHWw4dxlJTgs2LFgK9pqt6PV6QPFksaGo0vgYELMJlGYrNVEhZ28yVsrXC1OX78OAEBC8nLW9/j+IzQFEK9gvBS63gtdxPDjEZmD5uE3emkuLaEP6zdwe9WjkGrVpISGoPN5SCvvgqfgHDCwyfS5eqkOWIchI+DmL57ZRQKBcG3f4POTVtwt7WdtY0b897A6uzghuTHLmg5+oVQKtUYDTFX/MpHEfQIl0VJawmxltihboZwDWhZvRpNeDjGiRMHVF52y8iyFdQyWu3p/do0GjOBAfMuVTOFq4wsyUgON8nJybS1KQCZhobeb/Cd9gasjlbi/UYCsDh1FgcKc5gf76C+rbK73DfHLeDDY9uQTs7PUSk1dFrCIHbGOdvhc9MKcEuUvfv3Xudsznbey/wtI4OnMyHqhot42gtjMiXS2VmILLsv+70HSgQ9wiWXVZ8lAh7hspC6umj/ci2W5ctRDPATblVWEZYQf88O0oLQB1mSsR6tx3rMs1w8Li4OrXYUJSUbcbtPb2fz4YkNxFqiuD9pCatLdmN3OTjakM9tU5cwa8x1HCk4HeQAfCN1Ma8c/JJDmf8iyDeeiIjJ/bZFExyE94yZtH36BQ0Nud3HSxozWXP876wY9RThPkO3lY2//4wreosKEfQIl1ydtU4MbQmXRdv6DUhdXVhuumnA11QV7CB27AI0Gh8cjqZL1zjhqiTLMtasevQj/DEk+WHPb0aSJNxuNyNH3kZW1rsA7Czbho9XKFs6fDFpjSRYQnkjfwsN9nYO1nkyv4cFJHKs7HB33REWPxSNLfiao4iJmTXgNllWrEBb3EDu9neoqclkd/EqipuzuWXsD9Gq9f1XcAmpVEY0Ggs2W/WQtuNsRNAjXHJXW5py4erV+tFHGKdMRhsRPuBrJHc7OksQKqUBSXZcwtYJV7qmqs5ef6/sha2og7xQ6lQoDWoURjUnjuSSkJCA0eiHxRLD+sxX8TcGMT9sNL4aFfua20kNSOThkYuxaIy4ZYnm5v3kl24kIXxUd917d2QxByXD4s4vd5n3nNmofH2JLzBz/MgrBHiFM2f43f1ed7lYLGm0th2+Iv/2i6BHuCQqOyrZXLaZ443Hr5i8PMLXh9PlRK3qubWEo7SUrkOHzmsCc1t1MzqTZ/sAm60SvS5kUNspXD1KshqwtjsoOdrQ47gywkTehlLcDjeO2k5suc0oOyRcdk8OnpwOI1rv2YwM8MzhuSXEj5dLa7GdzM6cGpBIekMxrW2HuWHaU6w5tBWAvfsrMPsFEavXIbWeXw+jQqvFfMP1tKx+n8lJt5IYNPViH3/Q+fpMoqVl/1A3oxcR9AiDTpZlchtzmRs5F71az8yImUPdJOFrxua09Qqm29auRWk04j1//oDrKTy0nZjkKVit5ej1EYPdTOEqUVPUislXT3iiL8GxFk4cqsVh88zVqcxrJvHmeMo/LaQrvRZtlDdx/pFkb80gJ2cTaqkCL5OnrEuSeCq3nN8nRPBSSQ0Axe2VRHr5ExP9EAatgWHBYfxn7W5MRg3Jo4LRzl6Oc9dn591m/cRApNYupPIrM52CVuuP22294nJciaBHGHSHag8xPmQ8CoWCOEvcUDdH+BqyOqwo5Z5/vtq3bsVr2jSUBsNZrurNYS3DEp1MR0cO3t4jBruZwlVAlmUq3v6I5n3p5H3wCc7aYoanBVF1ooXK/GY0ejVaLw1eESZswUYMiX7IrU70ygrUSiULx95EWWcTrXbPLul+GjVRRj3jLV68UlxAVnMpy2JO98REuTuZFaNn9GhP7hyFUol63Dwcu9cOuM22hiMoAuyoQ0Pp2Lp1cL8hg8jPbxpNTVfWFhUi6BEGndVlvew7pgvXFrvLjlF3ev8gV0MDtqNZmObMGXAdTqsDpUpNZ+cJDEaxuvBapVAoSJofT9SsZILq07HW1VKxfTsxowPw9tMTnuADQOCUMNxWNzUHa/CeG4le10F8omcuzvKYqXxSehC1UsmyIB/+XdlAkq4Tpb2Im87IudN4Yi8KpZqoEeN6tEEVEglqNa7yon7b6+qsxX7iUywp38V7zmw6tm69IufOACiVWhQKFW5311A3pZsIeoRB5ZbcqMRGjcIl5nA50GtPr1Lp2O5ZImuaNfCh1KL9WQTHhNPVVYzJK37Q2yhcTRToAgPwuf9HGI5swDcyhBMff4Ze50RxRiqD8OnhaLw0lH5RjF/iPGqy1iNJEpk7X2Sily8byg8y3uJFqc1Ok8PJMNPpTUcbC/ajUKrxG9b3JsfaSQtwZ+1Adrn6PA8gu+10pL+EedJPATDNmYOzqgp7/oVtQno5+PlNo6Fx+1A3o5sIeoRBVdZeRpQ5aqibIXzN2Zw2dOrTcxk6tm3FMHYsan//AdfR2piJNkSNv/+5k8EJ1w6FwQvzgz/HveldYqePo3LnHpqOHetRxn+kP/k6BZn7O2lrUpC55TckpT6AtWgTyBJ7SnNpqm8gxT8ah+SioqOWpsKDKBTKswY8p2hmrcSxbfVZz7fu+zXek37cnYPKOHEiCqPxih7iUihUmL1H0dqaOdRNAUTQIwyyyo5KIkxiQqhwadlddnQaT9Aj2e107N5zXkNbngttaPQmVCpj/2WFr7nTw0MKjRbzt39J16dvEJmSiNtho3Tjpu6dzd1uicYOBzNvTKSpzYeK5jQMpgCGjbyFgNIdlFQc4jpvLVnlFSyJmsy6/EPIkqvfgAdA6eWNMjQOZ05Gr3Ot6X/AOPIeVGdMHVDqdJimTaVj27aL/xZcQgZDJG53Jw5H41A3RQQ9wuBTiMy2wiVmd9m7e3q6DhxA7urCNGf2BdcnOxy0fvoptrz8wWmgcFVTKJV43/9TrJtW4WPREZw6hoJPPsfe3MSmHWXMnenpzZ58w0QmzJzMe++vweb2JibhBu6cdjcLkkewt6yCAydqGWWagH/8lAHfW5M8Hqm6CKmxrvtY+/G30QZPQOub0Ku8afYcrEeO4Goc+oDiXHx9p9LUvGfI5x+JoEcQhKuOw+VAr/HM6enYuhVNeDi6+IHPy3G73SjPCM6tx45hmjULpZcXrZ9+iqu5edDbLFxdFEol3vf+APv+zShrixl+0/XUHEinpaaSkIDTvYOhwf7cftsNHDh0lKyCzu7j81QG2hrrmZwQdN731s5egXPf58gOO52l61GoDRgi+s7YbJrtOX5qXtuVSqFQEOA/m6amnUPaDhH0CINKgejlES49h8uBTq1DlmXat27DNHv2efUwttU2Y/AyYNt8iPatW7Hnn0Dl44M2Ipx2vZ7O/qsQrhGmOx7HmXcYR+YOohctIHWMF5s3re0e7gJQKpXccN0cAgP8eP+DzyndfxS108X8yaPOUfPZKZRKtPPvxPr5n3C3FGJKuPWsZdX+/hjGjLmi5/WcolCoUSjU/Re8hC4q6HnhhRdQKBQ88cQT3cdsNhuPPPII/v7+mEwmVq5cSW1tbb915eTksGzZMiwWC15eXkyYMIGysjIAmpqaePTRR0lMTMRgMBAVFcVjjz1Ga2trjzoUCkWv13vvvXcxjygIwhXIrDdT31GPPT8fV3X1ec/n6WxoR2vQglGH95w5+N52+k2lqbOTTquVvLw8co8cxum4spKrCZdCz4DZ3mWjue50lmSvFQ/RvO8w9fv2kpCcwqQJk1jzxac0N/R8bxuZGMvMadMorWgnekbPZenny2Frpy0kFfPYh/sta5ozh87du5EcV/Y2Kk5XK1rtwBcbXAoXHPQcPHiQV199lTFjxvQ4/uSTT7JmzRpWrVrF9u3bqaqqYkU/aeELCwuZPn06SUlJbNu2jaNHj/Kzn/0Mvd7TfV1VVUVVVRV/+MMfyM7O5l//+hfr1q3jgQce6FXXW2+9RXV1dffrpvPYeFAQhKvDhLgJZJRl0LF1K0qjEePE/ieJnqmrsxN3TRY+KfN6n1QoCAsLIzExkdriQqpycyhMP0BTVcUgtV648vScZ7JxzS727chk3+YDAFSu34cjdCySQkndoYOYLH7csPRGsrKPcCRjX49rSw7WMX35wOfw9MVl7aDpSAZBk/v4+eyDac4cpK4uuvYfuKj7Xmqy5ESh0AxpGy6on6mjo4O77rqL1157jV//+tfdx1tbW3njjTd49913mTvXk7TprbfeYsSIEezbt4/Jkyf3Wd9PfvITrrvuOl588cXuY8OGDev+/1GjRvHRRx/1OPeb3/yGu+++G5fLhVp9+jF8fHwICRH75wwVmSszSZbw9aJUKlGgoH3LVrymT0epPb/93Vx2J0pHJ1rf0B7H63Jy8I+MBMBmtaIzehE9JgWAooyD+IWJlYlfSwolstuNQqXi8MHjGM0m5i6ZTOHxQv73z8+Y7O9F7EpPANJwOIPqvXsJnTKFmbMXUlFawJZNa0EBGjmA0LgolMoLH0RxO53U7d9FyIwFA65HlxCPJiyMjq1bMc2Y3v8FQ0SS7CiVQ7sL/AX9yzzyyCMsXbqU+V/Z4yY9PR2n09njeFJSElFRUezdu7fPuiRJ4osvviAhIYFFixYRFBTEpEmT+OSTT87ZhtbWVsxmc4+A51TbAgICmDhxIm+++eY5Z4rb7Xba2tp6vARBuDrM8B+DNev8sjCfIrmdyAoZpbJnsFSTk0NoWhoAx/bvYcT4id3nVBoNhekHqCvpP2vutaqiooLc3FyKiq6u75HKYsHd1kbF8b2UlVcwd4nnA3rciDja/YcTvfz0z1hAaho6s4mKbZ6EexHRw5k7fwlz5y0hMUBDlKvsgtshSRK1ezYRPHUWStXAk7wqFApMc+bQvu3Kzc4MJ4Me1VUW9Lz33ntkZGTw/PPP9zpXU1ODVqvFx8enx/Hg4GBqamr6rK+uro6Ojg5eeOEFFi9ezIYNG1i+fDkrVqxg+/a+szg2NDTwq1/9im9/+9s9jj/33HN88MEHbNy4kZUrV/Lwww/zl7/85azP8vzzz2OxWLpfkSc/4QkXTkxkFi4X76N5gHxeWZhPkSQ7Sgdg7z0H4tSn666Odix+p+cfRI9OYdi4iXS1NNPV2nKBrf56s1qtJCUl4Xa7h7op50Xt50tr1jYUDVm0BdiRTk5SliUZSZJ79bj4JY/GOyKE4g97JhIMSknB3dyK1NZyQe2o27uFgHFTUGkHvn/cKaY5c3BVVWPPv3LTLkiSHaXi/HplB9t5DW+Vl5fz+OOPs3Hjxu75Nhfr1A/XjTfeyJNPPglASkoKe/bs4ZVXXmHWrJ7L9Nra2li6dCkjR47kl7/8ZY9zP/vZz7r/PzU1lc7OTn7/+9/z2GOP9XnvZ555hqeeeqpH3SLwEYSrQ/vWrdjio2nRSASc57WSvRWUoDCbTx+TJDhjBZij8/QarhOr/4JSpSI4bS6SLGG0+ABgzcpGdjownuwdulrJstxr9ZtLclHWXkZ1RzWNVR0kSmPx8tERkeg7RK0cHLIs02Z1UdXSiaqrEYUs4bZ14WrYzqgVf2RRax1rM7ezNG0OSpWSeRPCWburjCXTe2aatzXLKB1K2v/zB7zv+X73cf2sOVg3rMN43dLzalfdwe1YEpPRmnwu6LmMEyegPJmdWZ+YeEF1XGqS5ECpHNpd4c+rpyc9PZ26ujrS0tJQq9Wo1Wq2b9/On//8Z9RqNcHBwTgcDlpaWnpcV1tbe9Z5NgEBAajVakaOHNnj+IgRI7pXb53S3t7O4sWL8fb25uOPP0ajOfeEqEmTJlFRUYHd3vfqC51Oh9ls7vESLo6Y0yNcDrIk0bX/AOELr2dTzqbzvl6h1qEPTaKjOLP7WPnBg4SN8Oy03l5Xgdlb330vlVZH3A0P0VFxAk3up3ByCMHd3ITKYqHzCp9Aei6S3U7zO/+lKz2dts2b+GDTn9hZsZMDNQfQKrVMCZuCr9If/0Qtkkvqtz5ZllGpVLjOsYfUUNhT0MC67Gq259dT2NBBQ0s79TYF8fHxxCXGo0j0bEcSZAnCYjSRX1UAQHyMD51WJzUNPTfNbDheQfSdN2GYvYy2N37dvYRdoVahTR6J/WDPCc7n0nBkH8awCAwBof0XPgulVotxyhQ69/Q9leRK4Al6hran57yCnnnz5pGVlUVmZmb3a/z48dx1113d/6/RaNi8eXP3NXl5eZSVlTFlSt+z2bVaLRMmTCAvL6/H8fz8fKKjo7u/bmtrY+HChWi1Wj777LMB9TRlZmbi6+uLTje0kaUgCIPLXlCA1N6OacJE3NL5D6UoFBqMMaMo/exZyra8RMX2f1BTkEdAUhIAbblbCaSZws/+Qd4Hf8AvfiwKpZKQcfOIDNTA8U88gY8soxs2DE1IMB27dw/yU14eHdu343vH7Sh0enRxw4hu1zEjYgZTw6YS4R2BUqEkKjyUksqqc36kkWSZRrWW9fmF+Pv7k5+fT05ODtXV1ZftWc7F36RjbKQPsxODSIvyZVSkP00dNgC0WiNu9+mhzulJEzhUlI/D6TnW1O4g0Of0e07ZxnTCJnqSYaojE/C64Ru0//PZ7s1C1dGxyA4Hzrzj/barOS8Djbc3pvBh/ZbtjzEtDWtW1jk3LR1avXsUL7fzGt7y9vZm1KieyZa8vLzw9/fvPv7AAw/w1FNP4efnh9ls5tFHH2XKlCk9Vm4lJSXx/PPPs3z5cgCefvppbrvtNmbOnMmcOXNYt24da9asYdvJ/UROBTxdXV288847PSYdBwYGolKpWLNmDbW1tUyePBm9Xs/GjRv57W9/y/e//32Ey0fM6REuB+vhTFCpMIwexdhGIweKDjAxbmK/152iVOmQJSvaKSOJmvwUblsHefWvsXPDISw+3ijVNUQFSqDUoAq9DmPIyZ7oox9A6j3gFYB7/9uozJ6UHdroaBRqNY1vvoX//d+8BE986ajMFmzZ2RhSUjwHMnuXiQgMISe/iED/UGRZpqvNQXNNF44uFxEjfKmW3WxoaGN2UBB5hSXd8yRlWaagoOByPs5Z+Rg1NHU6CLV45stYvPS0W8+e12b5xDl8fHAzt01dwshoH47mN5I6MpDaHRk4Ou34jjj9oVwVFInp9kdp++ezmB/4CQqdHv20mTiOHsa6YT362XNRaHuPTLSV5CC7JSyJyYPyjIbUVGSrFVteHobkwanz62bQUyO+/PLLKJVKVq5cid1uZ9GiRfz973/vUSYvL69HYsHly5fzyiuv8Pzzz/PYY4+RmJjIRx99xPTpnqV3GRkZ7N+/H4Dhw4f3qKu4uJiYmBg0Gg1/+9vfePLJJ5FlmeHDh/PSSy/x4IMPDvYjCucghreEy8GakYE+KQml0cgo4yj+u++/5xX0KFRa3M5m2tx6SvP3ETFsPGFx8YxIG099TTMFx4MxT70XydlFzUffRaFKQukbgk5RicI/zlNH8jLkj36PnDwShVaPOiQEbdTVNyfQa/IkHBUVtK1di/fixX1+ElepVEiyjNlfT9mxJoxmLSFxZhxWN611ViIiTcR76Yj3MrD/jMvP9alelmXs9ho6uwrx9ZmMUnlpM/VaDBp2FzQQ7mvArNfw5tr9BPlZzlper1IRZd3PsaOBTB83nrfX5BHhrqYj9wjDv907sFX6BOB93w849vcXaUiZhFajwdtkIjZlLLYtm1FHhqJJHttdvrOmFHtzE4Gp0wbtGfXJI1FoNFgPZ4qg5ywu+qds21d2d9Xr9fztb3/jb3/721mv6WtJ3f3338/999/fZ/nZs2f3uwxv8eLFLF68uP8GC4Jw1evKPIxpxulVW1qVFqvDimGAq150Or1nKwt5Ol6WULIPfkhsomdZsk5rJyTs5IcrpRYpJA3j7O/iqiqi89M/YzqZL07p7Yf+1h9j/e8vMNz+Mzo3f4nX3OsH9TkHVfkB0HpBcO83Q21EBLajRz1fnONvrW+IF74hXt1fq9RKGsrbUSkUSCcvUyoUON1uNGcsuba2t9FYUYbT5hlOamzaRfiIYXibE9Bpg7DZKjEao7mU9BoVE2P9yChtZnZiEF5eRpZNGXnW8m57C/H+XhypqyemrZVpSTJZx0qZ7F2B1NWO0ujd65rj2aU0Jc1n9pyp2G1WOtpa2JV+gMVLluHMPU7X2i/RT5uGw22no7SI4Ennn27hXJQ6HfrkZKwZGXD3XYNa9+AY+pGAod0EQ/jaEcNbwqXmamzEWVqG4bGU7mOLkhexNmstK8adO/v7KQqNEpvdzX/dXoytUCKpp9Fcr2SmRaKseDNJY28BoOnYenyGzwZAqTeiCIjtUY/S6I3hjl/Q+fr3UVhCURYowOADQSPAO8yzGmyI5zAA0NkAbZVgiYKqTAhL6Xl6336MkyZ5embyNwIP9Thv7Wqiq/EE0DM9wFd7cuodTookJf/an4GPTotbcmMpOIZpwkSCYoeh1XuCUq8KJS67DZMpEbfbTmtbxiUPegAifI0U1g9sZzW1MQitdwyTAgx8uu5LokICmXvTDci2eVg/+j3Gu37Ro/yRvUfp7LAyc8lUAHR6Azq9gajwMMqK84lKGol6eCL2nduos50gaslDfdz14hlSU2lbv+6S1H3xhn4kQGw4KgwqMbwlXGrWzEwAjKmp3cfMRjNWp3XAdfhFBmFtbmVatJs7x0UyMkDC7mrj3YxKDrSWUNPuyXViayrCFOGZr9i2ZTW6uZ5gSJYkWsuyAZDaGrEWF2O86ycQOREkN9TnQfpbkPWh5+shtnfnBpoCJ0LEOE976nK7zzmrqkChQO3vjzM3HTnQD8l9eiJsxpF/cyj7HcZ5hXcfsztcZGTVsWZ9IR8X1LGlsY0TXTY+rWvh1mFRPDh1AreMG4tapcLbL4DQ4YndAQ+Ab9AIWps8c31UKh2y1HNujcPqonHHATp27KB1zeeX5Hvy1dGDU/GbLEl0tdfRmLsGydVF3Ym/c/uKW5k+05N014bMIV8/9qZndl97dH8WnR1Wpi6Y1Os+I8eM5/jJhToKtQr9nHl4x6ix1R++JM9lSE3BVVWN8yy58YbW0H8AED09giBcVayHD6MODkYd2nN5b5RPFCdqTxAfHN9vHZYgXxydVipbrby+ez/JQQF8VPgKI+Jmo9Sn0thRxImazfg0lHJq44nKdi3e+9/Hf8JK2ovTcds66CzPQnVwG3tSkxhTvJlhwxaA78keC5cDZAmyP4Lk5aAamj2H9hc1MmzaCsr2rcadPJXAtkqIX9h9vnPXDnxuvR0A24HN+Ey/kcbGPKy2ZvLKdpDgH020SklGYS77P4vFqFWjUSsZPtyXpQti+XNGGXP9zcz1H3jKD43WgOR24nB0UVWVgbWiEGPgaEqON2GM90WjV9GZV0n8g8tp27hxUL4Psixj73RxuLKV451W8lusNO/fQrjchgIF7qaDhMmeDUTzMvII9tIQOvMhYuPvQXFGcsKsA+8xbdH/Y8OugzQ0NFJTVE1bayfTF/a9zRJAXGQkRSdyiIv3pETwHfEtatN/icYch0p39nlFF+LUhwFrZiYaMeWjFxH0CIPqSk6BLnw9dGUcxpCW2mtoZXridN7d/+6Agh6lUokMjG6sJG3CUozeAdTVjmVu7HS8jZ43IZvTzp71/9d9jazSErn4cap3vYPkchAx91uU7FhLTTssf+Jl1m7/pSfoOUV9Mh9J9DSoOAjRUy/62QfizESDtW02mrucTPLWE7TgTo5sXYU7KoGQnM88gVjZPlQd+TiLj6HyDUZh9CI0eCyrdz7LpPgbWDD9x1Qe+AG+ac8huw9wQ2IkXnpjj/ud67O7Vq1GqdXR1tSEs6aN/GNZuI2evxFqLzsnTqwlOHgsHQ4DhkQ/jKXtRI/yZMEu2qfFXliILi7uvJ6/ODO9V/vyD1aTNHUKOi8NFhfcNiocY2oUf83fy00Jnn0idx0ow3/MowCM8j5KW8lxWvZ8gnH5j3vUZwqIo76xnEUzJvHWF5vIl0w8ODPlnG1KSE5h/frPu4MegMCxP6I+8zcEj//VeT1ff9SBgWgiI7EePoxZBD29iOEtYVBpVBoc7rMvAxWEiyE5HNiys3sMbZ2iUCg8n9gHOJykUCiYNO0WTmSsAWD++JtYv+/0xsZ7jh0ictIiTrz6IR2V9dQ1OJGBsJn3EjH3WwAUHTqC7+KV2KzNeBv8qf7kdRoPfGX7nIoDly3gOVjSxKpDFVgdnu/BvsPZKO2n9xQcO+cWGqpKqNDHw75/gNuO9/0/x7ZnPa1v/g6vGx7AbInkG9f9kxGJNwKg14ejVOuJ8g+htL73vlJnfs6RJImy+g42fL6OzI2v0FVSgKOwguoPD4BSQaBvNNOXLmP60mVMnv0wyckrCQgYjvIs857sRUXohg08f43b5UQBxKaM637FpIzDEuRFVLI/wTFmAkw6jJpz72tljh2D5HaiiUylbvs7Pc6NHDmH8oJdyLLEAzcs5PkbJvPvrMru3QXOJj4mhvyco91fK9V6fIfdTWPWnwf8fANlSE2h63DmoNd78Yb+Q7EIeoRBleSbRG5Tbv8FBeEC2I8fR3Y4MPQR9ADMSpjFpuMDy9Cs1gXiaKpFklw05ZZR8+URYvTD2LBnDdsPbqS2tZb4KeOIvmMJJf/8gLQ7l7Pps920t3omwpbu3QtyB4kTFrI38w1iwiZQpbaiMhhp2HNySKZ4J0Rfnl2v2zodlDd2cfO4CDbm1HL8RBFz0xLx0mv4Yn9Od7lRM26krbGGuvJciPP0cnjf8h1MEaDQeRLwKXrsNeV5o4oJiqK0obb7aG19M19sT6cyu5hP95fwZWYlH2/PIf3LHUQ6W4nK2cvKkdHMvOcOgoJM+CdFo1AN7C1HkiTaHG5czW2eeT2ff0FXRsY5rynef5Cjm/qfwHu2t92v9hxGzb+HxsJsUCjoKM3qcc4/NJniEk97lEol30qJ4C8Z595oNC5xFCVf2WVA65uI1juO9pLBnbdkTE3Fdvw40snVcsJpIugRBpWP3odWe2v/BQXhAnQdzkSh16M/mTn5q8L9wmnoaBhQXRqvMDoaiggJHEVncQXDVkwjcdhIIgt1JAeNwJDZQnlOCVqzF6OefQSfED/mL5vGzrX7KT1RwIntG4lZuBh7ezWSs4sdOZ/SETkBn9ET0PgG0rj5fyC7wRQ4mN+Csyo/1kRdSRsul5sJkSYOFNbj7e3NtORY1O1drF93+sPIfq/RHIm9kYKTb9x0NaAxKaCP3gq7205m+afsL36DvUeyefeLXbz35W6O5JUyZWw8P5mZwBS9k6CjexmvtrH8G9eRsOgGan1noRmeglKnAbcn1OirP6e1ooLi/EwO7NhKes1uPtu6hbU7dnCkYx8KyYlp5kws1y9FdjhxNTYiS73DFmtuE4oKF1FJY7F1DWx11pryw5R2dXR/rVAocbt69lLHLribzvYuWjM+R3I5oPIwldX5dLbXMXzY6bxQUWYDoSY9uyubz3nPpOHDyMnO7HHMO+Z6HO2FOFpODKjdA2FITQWXC1t29qDVOTjERGZBEIQBsx4+jGHUKBTn2HfPR+9DXVsdQeagc9ZlsQTT3p7OsCm3Ute5ja7mDooyi2jWWzGXNzFj0TyqqutpqqwFNJj8vBmWFo9ZoaHy4D4k2ogb7enFsZijKKipY8pIz8ajlhEptBn9KO/04nKkKyzPbSI8xsy9o/1547M8Ukaa8DGbus/7dVnxSYxi87ZiIscG0+xw8c1Ji8jL30Nu3i6SEqfDpO/Ahp9QELSYzsJSqjUleIX5oNP4EGUZSUrkjTTW/osVM3v2XLmNRiq2ZWL2DyR6xjgAVEYj8lc+U9ta7VirrGz595cEmGzdy6VM/v5ExnkTEhdOW7SbIGMAlfu2sPIbT9L5jzeQJAmlUonX5EnUvPcJ9SHjiR7tj9nfsxrMlteEJsSLqOETKfpkD0k391xW/1WnOnRKrDZ+n3J6DpZR70N7eyU+vqfTEjQe34cpLBaf+JXUrPs7QaXvUxy9jOnXP9Or3luTQvjVrgJSg8xnHT6LGjaCDes/Z8SolB7H/Uc/Ts3BnxIy4dfnbPtA6eLjURqNdGUcxjh+/KDU+XUhenqEQTfUe6sIX0+yLNN1OOOsQ1unLBy1kM05m89ZBiAwOpi2plYUCgWV7c2UZBUwesEElN5qtH4mNF56Rs8Zj1qnorqgArfTxbpX1xObEkNHvJGR9z/SXdf4lPvwN41Eqzm9maI5OgpLsJHS7MYLf+gBcNodtNa14hNkxGjQEB1sItTiS3SQhQ93eoZlUhZMpCUvj7gIb367IYcn0jw7hg8bNpHj9XvoyPkP+A/DmnIfVBygzn86xvCpTEr9DhNTv0OI+eyTw1VaNbh79xAV19Sz6/N17P5yA6VVbhqzGohdNIr4iDBix09nzPLljFm+nLiZMwkdmUpDWT5jAsfwZdbbhNTVYbIE4nvffTSezOjflFWIVdIzenYEdSXtSJKM7UQz6iAjah8dSrUKv5RYsj7bcc7v16l+IrVCic3tBKC17jh1zQVYLKdzBVXv+RS1wYvA1PloTH4Yo8dSY5lOVk0nDkffm1h/OzWSPx8qPef9RyYmkn3kUK/jpqBpWGsHZ+NahUqFIWUs1sOXZln81UwEPYIgXBWclZW46xswpJ076NGoNUiy1O9KQr2/CVeXZzgjZlwqmtB2lEolYydNIX3Tdur25mJraaep4hgLv309CZNGMP+b8whPjKS6pZKIoNhz1g9g9jfgH26i+OjAhtwuRM6uLbTXV1FdUAmAl0FNaXU7qcMjULqdrPnsSzQGLamLJ5G59yC3jYvgd8WeHC6fle0hxr8LWWemPft1cjL3EnvdkyDLhPiHUVNbedb7ulxOinKPc/izDbgLsqCpgOIvXiPn3RcAcE6azPTrF4MsYwhSED49HGOAAd+EcJrzynvU5RcSQ0ddJQUtBeitzcR7BQOgDQlBGxdH1dvv05KVT+ydntVIMaP9yV9XgtrfgNrXMw/JaXNQd7wGq9lFVVlJj/rP/Bim1ShptzkJ1hmp6mqlsbmYfemvsGDaT0/PZcr8Hy15Bwgad3ppv8/oOUTc/XvuXHQ3//nwj72+Hx0OF386WMIT48+dZDEiJp7KPnLoeEUsoLN28DatNaSmYT18WKyo/QoR9AiCcFWwnlyN0r0x5jlMiJ3Aztyd5yyjUCpQqH3pqC/GzzeW1lbPJF1viw9+kdG4u5x0drZg8vVDefLNUK1Vk5G3m9FRaT3qKq2tJNQ3pM/7mHx1BMeYKcyo6/UG1GZzXtSbUl1FFj4hAUxZOY2K3ArKc8qYNT4clULJ+t2lNDW2Mi5lJNu++Iz2zg4skQZym2u4wd+b72UfJVBvZkz8D6h0ZlDc2YFfXDIqtRpvkw6zIZjK+jIkSaKkuoLP928iu6yYneu+ZOe6L9m/xdObpvbyI2KUHwn3PEDs0gcx+AScs83GMH+sTR09jqlUakrbSslpzGFCYFqP2cbSsNG0dSqIu3NJ97HSzWUEDvdB7Xc64Clcf5Thi1OYOHsux9IPIblPr+Jzqd2s2byZtTt20lRTyGsnDpLf3sQH2W/y592/ZKHKgsJxsk1H3weliuCJ11F74Mte7bdEJjE5KJwvNv27x/GyNhuxvkb0/awMAxibnExm+t4exxQq9Tm3ADlfhpQU3C0tOEvP3fN0eQ19ACaCHmFQZdVnEe196dPJC9cee0GBJymhr2+/ZROCEyhvLe+3XFDsJKqyewZHJRknSJySQuDkBPJXbWXMvCU9zh+vOEJaYs9NItMLjzA56ew9UEazlogkXwrS67on4jrdEuuya1ibXXNBgY8su5E05dSU76G9uYIJ10+ivrSOkqNFTB8XSvOJHG5dOJGwqBiS08azbvWHzJ45k/+XkszmgiKGU8aM0BTUaiORUU9R6NQQkzABAL3eTX1FGxnZxXyybx01LXXMGzudhWHLmTJ/ETMWX8e0hYuJSxqJStnP24hCAUolLptnSEipVOJyusjdk0nhYc+qMldpLl3tpTww+gFPt8zJIfKWE5XUpBeQ9PCt3dVlritBAfgneH4OXA6nJ+BZMha11jNNNTwqmk3/+0/3NcpwSEmOYcnMGdy5dCZPjZ7Ko9GJjHV28cvr3kIx+8ew/XcnM2hLMOZW/JKn0VlXgb25utcjJc+/G3V1GUfzDtBodfLOsSpGBpiQZTje0NGr/FeFRMRS29B4SXth9EmJgOf35sox9FMfRNAjDJqcxhxUShWR5qtvp2nhyucoKkQ3bOCJ6nyMPtQ2156zTFTKMJpqXXQ2lRESEk9J6SE6W9qxhPjhlxTFuPtXUrR6N131LQDsO7oTk7H3aixJklCpzv0JX2fUED3KnxPptUiSzNbcOm5MCWN6fABrjlYj9bEq6VwaG3cQGDCb8Qu/y/F9q6gtzSNt8Xha61vZu3oHY+NDMHt7NgctP5HHjDlzUSqV6DVqwm0FRDiPUNPmWTH0zu49LJ30YPezHMgvwNxYz6KgOSwcNpvJI9Iw6PX4BflQW9nPHCWFEtnlRKVSYXM48Qrwoz7nEHs+PsRf3z7IZ+sKqTfoMflbqMk5gbu+kq7NHzF8whTP9Se/Dda8PI5/dIARd57elPPo1nJ8I0woJRlHhwOXw0nB2iMnAx7P5PbcnBwaqquJHjaMwkP7AfD1CaSppa67nq6uBran/4PF03/uGdLSGiBxCbjtkHJHd7no+XdRteeLPh9z/q3f48DO9byw5Sh6tZLNpY0YNSrqrAPLU5Y6ZiyHD/Xs7dEYwwdtFZcqIACl2Yy9sGhQ6hscoqdH+Bqpt9Yz0v/suxYLwsWwFxahjRt4orpFyYvYkrflnGUUCgXho2ZTvO9zwsJGU5Kbg8l4MlDYnIHaZCB6yXhq93uWe8sFdjQqP7Zknp570V9SujNp9WriUgLZsqWUCF8jOrUKs16DRa+mzeYccD02WxUajQ8qlRGlUonOG07sO0B1QT6jZ42mqeIwI6alANDa1EhzQwMRJ793e49vJNQvmBtSf8Lxys85fPiftJbW4T65rPzjTQe4fmYKsuzp9CjYfDq3TGhkILWV9V/5JipBpcLZWU/rkb+gcedweNM/qGnIRpIlQpISabl5KTPvmIZflIZli4cxe+U0IhJjQZLJ/b9/4n3fMz06AVwtzbSvXYd/rB9dtU0AHN1agTnAQPSoACIXxVC+oZSCtUcYtmhMd8CTXVTF9uNVuPxjSJwyg8oTOez64B1Wf/IPIkM9z2/tamLrrt+yePrPeuYjipkOKT13Jq/Z/wWhk3r29J2i0hm4de4yRhWu44ZoC1l17fjqNcyO9BvQv2FQaCQNTU3IZ/z8eMfcQHv52gFd3x+FQoEuNhZHUeGg1Pd1IYIeYdAoFeLHSbg0ZKcTR1kZ2rj+Jw+folapkZBwu8+doTlyVBw2ZzzVmZ8xcngaPv5Oag7lobEYKfxwJ6VfHCRq8XhcLheyJLN04jz8TD7szPL0IuSWFRAfOvAeKEmWKe+yMTLMs1dVaWMnW3LrqGw5+4apTf/+D7Z8zyaosizT2paJxeIZTis+toawmFlMv+0eyrIyKTpyGJ1Rg+tkzpkDq9YwafYiz7WShCN7D6EGz7m5I56kqrmTpbPG8OYnOzh2ohSdRk1YsGcriIQlMZj89d3t8LZ40dLQQuGBXLK3ZpC9NQNblxUCInCVZKLQeBN+/e9JW/wYQWOTMep0hJi9aTn5b/DVlZ2NVe3oJk38SjJE0MUPw++WG4i/ZQYFnx0ge0clJl8tMaM984UkBeS11RA7fxQavWfF3PGSagoqavjOynk0tHRQW9/M1FvuJHHSNObMvpnyas+b/9aDf2Lx3BdQqvrP2GJvb0EfEN7reOm6A5R8uZ/6ox1Miw3nsw//wxMTYlg67PxyMo1PG8ehg6cDaKXGiCy5znHF+dEOG3aF9fQMPfEuJQjCFc9RXg4uF7rz6OkBmJU4i83H+l++Pnr+DKpL7WhULuxNdXSW1xMyPolhN88gePoIVGo1+1ZvJmWhZzuJpo4WEiI8bcmpLGB0bN/JEvvywaZibp0fR22bjcf+d5h/7ijiFzeMRKlQsLew99CR5HCgDgxA7urCdvw4dXVf4ufrGQqydTbR2VFFUIRnYrX3qMmszSjCZ8xUtr39BkVb95M2dy4FB47TVtdM4a7VpM25j9qm/O76l859krLyHGakWEjPKeX6OZ68LgqgrrSalsp69mw4yp4NR9m74SiuxjZCEyIYNSeNUXPSSLtuCurIYSjbZLCdLTmfJ9g5NYdFkiRWbyoi8vpF6MwGJIeTYL8Eduz/E/aKA6jqd+I6kYFSqSRq3liaswuJS/HkXXK7JT7YUkxVjBqNwRPw5JXWkJ5fxk0zPd+HmxdN5pNtGSiVagKjY5mcMp+CwqN0WtuZnvIt9ma8etZ/H3t7Eye2rzr5Ve/hmJoDOTjbrEQuHEfs9ZORrbF4NzdQVnT+mej9AkNpbm3r0dszmHTD4nAUFV1BK7jEnB5BEIR+2Qs9n9LPZ04PQIRvBPVd9f2W03np8QkfQ0NxJoR6o0vwvAkplUqU3kZ2/O9LhqUlYzR7hr6aO1sJ9j29SknZ32TekzZk1eAwKvEyanltZxF/uGUsj8+LR6lUMiLUTPHhg+Tv39PjTdCWU4rSEowhJYX26kYai6rRaDyTeHMz/83I8Q8A4Ha52X2kkEcfvI3mDivGuBSaCyrwj48iZfFkivccRqvzwjswGrf79FCay+kg0i+Z7bt2kBJ8ejNRGWgrbCD13jSmLhzD1IVjmLJwDBGxIRh9Tic+BFB6W1DYemZCNqp1tJ9aEYVM28mJzP/aVcxbn+UzZUwwqSMD8QoOpKOyjOHDFjJz0uPMnf5jNEmzcdVUAeATF0pEjIHmnFLcbon3thRzw/Qork8bxceZWbRW5NGw5x3mJfj0+Pe4fsZYPt50Ou/NDQvv48uN/8ZsicTXHE5pad+r+46sfRuDtw/1Ofux6XqvRLM3tjP81lmo1GqUahVxN01jrHk0uScubBhp0rgJ7N93ui0qnS+uzt5L2i+ENjYOqasLV+2557ZdS0TQIwjCFc9RWITSbEYVcO7l0H0J8wmjqLr/Lv648Yl0OWIpyfkvAXFj6GhpZ9s7n5O1ZT+TbpxH6PCI7rKnNjV1u90MNBen0y1R1mWn3aTi1xtyWTAiGK1aSZBZz7qNu9i0dS9x0eHEjEkhd88OnKf2TVL64KxqoPNwHiX5VkyBaRQe205e+ipCIqd1D9N8uHE/K+d7MiIvmDuF9AobhpkzAM+wVrzGimr4aLKOrqNx5xH2rf47+1b/nYwv/kXLjjwe/uaTKK2Qf+D01gVGf2866r+yrcw5ew1kGtsa2Ju7hY7KLawq3s0HhTuI9Dbx3N6DNLo60HupmZASTGiQJ4D0jo6mo+KMFVIBCShaK3rcJ/a6CZTvzuX9zUUsnRaJ2aAhxOyNXu6kLD+DaXd8n+qcPbicpycRh4cE4G3Uc/yEZ06SVqMjdewstu77BH9LLNUNx3u1viJ9AyGxSbhdTppripF8Itj2xWd8+t9/s+2Lz6gs7t1rotKqCZqfgrq+EVtnW686+2PxD6KtowPJ5fmZMkcvo730s/Oupy+nPiSc+tAgiKBHEISrgKO4CF1s7AVl+56dOJu9pXv7LadUKlFpTGi0CjLX7+LIxr3MvH0JU1bMR2fU9SgbFxzN+zs/489r32B01MAm779/uJJbx4TxrZG++NVsJ9Dl+fSde6IEg9GAQW9g8oTRaA1GkqbO5NNP1lKZVYcu2oLvzQs5sHcLzmEGooanoVFZaC2oIiTKs8Q8PacEX28jfj6eeUIf5tUwfl4KkkLB0dw67Du3o588gaKcrSQkzcI/6RYmr3iYySseZuJN3yYwKBiVSsWo2eNxO1zk7MpEpdNg9POivarvIStnp4OaQzUc+992Sr7cz6Fj+eS1mymsOc7IyDTUqnHcn+iZS1TlLiTAt5lHF0znxuRQKuq6uuvReJtPB3iefwjPf8/4t5YkiVp9K4vGBeFj9AxpNTRWEN15jNFzPautkmfdQtbWD3q0cfbEZLYfPj2UNzx6FC5bFznZXzB53Hd6PVNdTSVRE5agM3ojuV2MmjCJ2UuXceNd9zJ76TKaGxupbO09BKkKj2RcUBD7D/T/c9aXyRMns2/vNk9dBn/czv6XvQ+EJiIChVaLo6h4UOq7GFfKEJvYe0sQhCuevbAIXULCBV2rUChQq9TY7Db0Ov1Zy7ndTsor0/FxpOL09mLmrXPOWnZ8whjGJ4zB5rSj1+jOWu6UrIoWws16zAYNh/Z+zkN3f4tD+3fhPJZJ5rEKbr/1+h7lDxw+zrCRI6mr66RdkokKN1MYrCXBT8+hY9n4HzMSN2oZpesPErlgHNsOHed791wHQL3VTmmLlZsTPckSD2Xm0K5REegfhLfRgk5r6HNmxak9rkZMT2Hv6k34x8fibGnD2tJJYU4ZtVUNyJJMXX49XvYSVHoVPgl+lFbKJF83iSOfd3DjrHk96myuP8H4gFjCvYJZV3EQt+TGoFXjdEmcqG7nwJEavFHi19xA6/bVjJm1ovta2daFLEnIwLZVHzPx+kVYTgZ1AGVFe0kcfzp/j95owi9qBIVZ+xg2ejIOl4tffPoR1w/r2Tu4YPad5L73IrIk9ZhALUsSZm/PsF3IqBmEjJrR63sUEhCJj77v3katSo3TZff0UPUTnBflH6e0ohwfs5kRI8eQn3ccg8F4zmsuhEKlQhsTg/0KWMElSQ6USm3/BS8x0dMjCMIVTZZlHEVF5z2f50yLRy1mXfa6Ps9JkpusrM9JT/+YGctXUu84gEp59g1NzzSQgMfplthb0cqchEDyC/YSEj0OpVLFxCmzqCzIZ1xwz9VlXVYb5aXlpI5JJHVeLJIkkbk6n+XzV1DYWoijy067pZWA5Fgsw8P48rX38Pf34UihZw7MP9LLefzkVgiyJCF3HSJw6rk34dT7emGraaS9tJjyrVsx2DuoOvIlnavWUHS4GLVGw+TZKUxbMI6ktLGYUwOJmB2JKcyr72d2SRw70kLm7heJ0PtRuOdtTHk7+HjnTnYdPIyXUU1Lh4OaQC3LFg9j+h2395rMa1x6Lx3/e5nP3/uItIXzsPhYepxPGbeC44c/7XEsOmkcHbXFtLQ08tst6/nRDcuoqyumrbOlR7nY+XdSvPbNHsdctg7U2rMHxQDlx4qJGNX3CkKlxUxCaATHM8/d25N95CDNzU3MmbuI2Ng4MjL2ExYWTur4Kd1lVFrL4M3riYvDcQWs4HK7O1Cp+v55uZxE0CMMGsUVMDNf+Ppx1dQgdXWhjbvwoMdisNDp7DnRVpZlcnLWc/Dgh0RHT2DixFsxmfyYfP13sHYdIm9f7zkfF+L9w5XcNiYMAC8vfxprTyefm7X0JvbVbmV3+uncLJ9/sYXrl57uMclqOYpqWCP+Fl9WTrmenJY81BEasjPT6TJbUI8ej9ZfT1FNEy9symJcuAW1yvOnPTtrPcOTF51ujEKBJLnpcFtJX/cO+1b/nd3v/Q7n8XeoOXgQR2sbYVOmkHLHTaTc/SgBI5XERPoQPTwU5ck6g6K9qSs+PXflzN96SZIoLWvjzU9ymBcTTVDQAkpyv0QhG0jQTODmWbNp7bIyPyWUCfH+qNSKPutBAarAMLZpG5FHB+Lj69Pr+6pUqohJmsPejX/iwI7TAUzyrJVs/PwlnpgxG4veyPULvsHqT//Ba//6Ba3tnpw/uoAIjrVUUVx2erm4vb0ZrdHU6z5nctgduJ19p0DQpqQR1NBIVcPZVrBB+v5dKBQKxk3y7FTv4x/M1OlzCYvs+bNtjruZtpJPztmWgdLFxWEvGvqgx+XqQK32HupmiKBHEIQr26k/2LqLCHoARoeP5mDhQQAKi3Zy4MB7BAWNZNKk2zCbg7vLBUWOJSYlnsLDWeeVePBsarocmHSebM3hoQl4WUIpKPLssr07/W/cfP3P0Wr0bNv3GQczjjN8eCx6nWcYYMexfYyNjyVhTBKZ2zdi0hu5c8YK9lQdQ6XVsP/QYRZPTWBm4nBaXa3Ym4rxVnjmxzQ0VSI5bDQU5XJk6yqObF1FdUkVrx3bRnaEm4TJi5i84mGm3PI96qbFE3fjdfiPGYtK/9Xejp4fZrz91bTUn55zolAqcblc+Pr6UFPXhFshM2a4P3FJUTi6zCBL4CXTUVU1oO+XLEm0d7VzNPNLho2ZRGZbJtWdfW/YGugfycS5jxCXvJCsrPVIkovsnb/i+hVP4mPw9Cpo1FpuX/k499/zM9Zt+S8A63e8z/BJ11NYtg2H05MfydHZgtZg6fM+p2gN2rOuupbbW1GYTJh9AmitKel1fteOTfj6+pE8Zny/3wOVzoLk6uq33EBoh8XhbmjA3draf+FLyOVuR60+d1B5OYigRxCEK5qjsAiFVosmIqL/wucwJnIM6cU72LfvXQz6ACZNugN//763TBk+ZilG/0r2f7Lnou4J8O0JUfxtb0n31801OQyLSaO8fC/+5igMRj8mjJmDwctCev4B0lJGAFDdVE2Xw05SRAK+gWEEWbw4tjobnVbLvbNv4a3SA6TN8EyijvAxE2nzZanRD6vTxabCEj7YuQ21U4d/+DDGzrmFsXNuYdS8e3FLkYT6hOLt40mkp1SpkeTewZ3sdmNrqKC9IousPVu7XwVHDqLxPp2l2Tc0kIayWvz9fWhsaiE61JvK+k58QwNwdOpwOtqQJAdyHzlv7O7TifgqdUEcOLCaI5lrqIu/Hi9LMCqlhflePnxn6w94fN/rFLb3Tj+gUqkJ8I9AqdRwcMNPSZj0GAZjz3k3ep0RlUrNxHELeO3tXxIRFseI4WnMGv8oOw79BfAEb26nrVf9Z3J1VeKu6b0XF4C7thZVYADjx03kUPbpXkJZkti44QviYuOIS7j8GetPfVgY6t4eu62mO9XCUBITmQVBuKLZiwrRxsSg6Gdvq4GIDfcmKXoOPsbQfstOvv4+tr/3HzLW60lb1P+n87NxSxL+Bg2vHc4m2lbA2ISZyLJETslmFs74aXe5Xa0NPLT89L5PW7IzuGO6ZwsEt8OK0mpl+Lw0jq8uQDs9lNTw64g2eTInZ+dVEBsfQlhQLNlb06mJD8eqTeQEBob5BHXXGWk24HLqaLaf7qmRZRl7RyN1W99GOuO4VOLAHRtA6oIHUH1lrkv2yZVGACFx4RQdzsUnMZqKyhpUaiWS7FkN5xnyVp66EdkbNtDe3MZ/jno2GlXV1tNhDcdk0KGPSEZSq0gJ8wGg09pGUcF+ZGc7M0LG8L20+/lt1sf8ePTyPvMiJSfP5TkJxhnOvg1EbEQS99/9U1Qnl/lr9GaiAkZRXLSZ2Lh5HFv/LwKTJvdZf032LqJGhOI8lI5UkIdCp0c35fTGs+7GRnSp41AoFaBUIdmtoNLy5fo1TJs0Bd+A4F51novOHIu9IQtdwOjzuu6rtLGxoFDgKCrCmHr2TXEvJbu9Do3WF4Xi4n+HL5bo6REGjdV19jT6gnChHEXFaC9iEvOZ5ibcx87iDwdUVm/0I3XhbJrq9rF39c4Lzpr7YVYN/mYXk0ODGO5sRVGRy+70vzM99dvdZbYWZjE6eBheOs/E6PWZO5g7Kq37zdfaWIPaaMDgqyd+UQzrD1Rw80jP6qxOp5WczqMY6zPRmwzEpiSS1NLF95aMZ8aoGFbvPtZ9n4aONnJqKskvyODD7W/y4fY3+WjnvwiwpOE74QZCFj/S/XJGT0MdMbJXwAP0GOIx+ZmxdnTiY/GmqbWVTfnHKOnw9IbIyGi0JnyDkkGhQO7q4vbbbuaeMSO4Z8wIFiXGUXhyL68xgWaOnTFs5mUw43Z6hnjMGgMOp41bIibwav7ZM2w/HD+RP+WfeyKx6ivbTyTEX0dB9QEkp5Vhk5dS0J2NuScZBXovC1433Yh+9jykri5k1xnze5wOFEbPCqxx46exc8dG1nz5KXNnzT3vgAfAFHUD7VWbzvu6r1Lq9WjCw4e0p6e19TA+lnFDdv8ziaBHGDThpnCKWod+wpzw9WIvLkIXOzhBj0alRZLdSNK59+M6JShiLOMWL8IpZbLzvR3k7MrG6fBkM5bcEk1VbZTn1FCRW4Hb2XvPpG359SQHG6m12hgdEkTczG+A3otDjZUYTZ4emC6Xg1ePHmJmjOcTfU11A13NFYT6ne6N0vsG4zqZy6Yip4gbhhlYlV6G3eHi09L93Dh2AT7xI6nZtYmSoyeIm+gZIvM3e1HXepRPD27i04ObeG3nn7k5WslCYzim7VmMLFMyslSBqgjc9AxuTg1G9TWvSZYkastPkL1vO5mv/Bd78QnKV7/Hwdxt+HuZiA06PX8kIeU2wmIm9/n9HR4eTFmNZ76Ov0FDh9OF2+2muKCIV3f9j+yGdA4V5KIv62Lbvk/J3reWAL2F/fV9L8EO0Jvw1+rJbKrs8/zZTBv/CDsP/hm9JRBzYBh1uft6lQkdNY3aotOJG7VjxmDbsglXaRH2PTtx1bd0n/Mxm7AEhnHDdTdiNJl71TUQCpW6n0SQA6eNix2yXD2dnQV4eQ0fknv3RQxvCYNmhP8INpdtJswrDL363Es/BWEgZKcTd0MjmrD+h6MGakr0jewu+YAZcXf0XxjwDYxnwmI/8g59TENNLjl/2YIlMAqzbwgmXy0Gsx5ZVpP+5SEcdidTV0xFqVbhdEsUt1ixq9v41tgR3fUFJU3Hv0vDL9Pf5wdjl/P33A28tOAWnttdwI+nDmP3oQZmJERSn7+LwATPKp+G9N0ETZ5DW20zkssFnTamGrT86+BOJieGo1VpwC+EPP8McjokzhzEmBunYcyY+Z5nyc9nZsJkYDIH2roYufw+AA6/uZZjOzKoOtFAYLTnTVquaCTCz0xLbT51rQU4HKdWbCmoaCjGN9ifpHFTqWt8jZSlDwNw3cevkBoeTXHpyd6lr7xnfzVBXUN7DellhfiXePFFOaQFm9n6hz8StfQ6RtU0Me3m5/hox1usnPlNAD7f9G+uj5nIC0c/YbRvJEZ1z7wvFV3NlFs7uDfm/H5ejDozoaHjKcz/nGGjridvy/8whw5Db+m5gajb5Tid30eWUWg1yFYrmjGp6Kb2nKSbknLhQ6Jn+mo+oQuhCQ3DlpU1KO05X51dhQQFLuq/4GUigh5hUM2OmM2msk0sirlyfsiFq5erqRlk+YK2nzibIO9Y9pR+2n/BMxhM/qTM/hbgeeMrz99JS/1xHJICR7OM0RJA6pK52Nrd7Pt0H2qtij0KF6oQC9NCAlGfMR+p3W7HrfLm6ZE38fusT5kQmECYycTPpw3np58f4/uTYwkKTaKtMpeyja/iHTsFY0QUSpWawoxcUpd48rmUZOQzVcpjdIAniWJ9az0bC5XMGjGGt3bv596pE9GoVAPKYu3tayF41HA6ShSMnjEWL38TeXuOcHT3bkKVLSSMmIfJdOZeY+vwD4tGrdH0SMTX614nv+xoaqSo4gRqFRTucIPG8/0I9AnkyZsW8k7mIYINcaxMCGbXqFEkjBpBc8F2oPeWn++uf5d5CSP4a846fjB6Wffxqq4W3iw6wk9HzhzwXmhnSoidx5Z9LxHRWU/C7Ns4vvHf6AwmwkdPQ2cOojx9PbauTlqri/AJH44qKARVUMh53+d8eIVMp7NiA6aoxRdVjzogAFdD3yvgLqW2tizM3hc3J2mwiaBHGFQqpYogYxDtjna8tUOfk0G4urkaPPM91AGB/ZQ8PxHm4ZQ0ZhLjn3Le16rUWmJG9sw83NZUTt6hT3E5bBgDYPjYldSv20pTRT1JidE9yr53/AT3jk5Cp1bzi9RbAM8QUnungyXxAYSEen5vVJVuTFIUHVuyCL97JXm7sxiWdno395i0BIqKqzlxYgPx8QvZmHWA565bwmtHK4gKi+Pt3fu5e8rEXu132jspzl1NVeNhMl55HHtQItbiSporE4hPHUd7fhleU0bicrvRRgWRMm5FryAiICCa+voioqLSehy3uZ18WXGEA51FsLuYykYHHe++jZ+/P4HzZmHwtTBmeO82Ab1Wd50t71drSyV5RRLjk1L4tCyDG6PSqLW288/Cw/w8edYFBTynzJrwGJv3PM/CGT8jedF9uB02Ko9sw9bRSuTYmURPvO6C674QhuCJNBx56eKDnsAAXI2Ng9JrNFCyLGOzV2E2i6BH+Job6T+SI3VHmBja9x83QRgo98lPp+rAwevpARgfdT1f5PztgoKevpj9Ihk19S4AHLYO0je+wpyl30CSlGTu24vdbmfM+AnkyiosWi069ek/vW1dDt5JL8OkVnH3JE+AJNnsWLPyCHhgBT42O83vbae8tZS4tLt73DcudhalpXt4a93rzB23EKVSyXdSovjxtjx+Nm0C7+w9QKqiloajLwMQ7mznUNZ/iG1v4MabnianIYuogBF4Lwkna8sqgscnkvnqOlrLG+nUu1FoVH0GEb4+0Rze/18M1g40vkFUfv5XFAolW3Sw0suf3857GKVSybFoLZ2OBFLjYrHautiU8clZg55TFIDU0oBclMNnn/+cakmDw2lHq9ExZ9pyTmQdweht4XDhMWoDLBxrqeKPx77g1SkPXFTAA55JzsnDr+fI0XcYO+ZuVFo9URMuLuC4EqgDAsDtxt3Sgtrv7KvbBlNrazo+lsEZ4htMF/UT8sILL6BQKHjiiSe6j9lsNh555BH8/f0xmUysXLmS2gFsa5+Tk8OyZcuwWCx4eXkxYcIEyspO54IYSL1lZWUsXboUo9FIUFAQTz/9NC5X78mFwqWlU+mwu+1D3Qzha+BUl/zl+kM9GLR6ExOve4yS41spzdmIl64Ks6GGXV/+m65Du2isquDNI8c5Xud5tg3Ha/jO1DjunRLb/abd/N5afO/w9Coo9Tr871tIREgCmz7Zjc16+ncrN3cNhWWHUKpTiA6MAmB/dQtjQ8wYNBrunTqJLbl6VGG3EzDmSYaN+zlTxj9EyIibUFRnEBY8lpq6o+gMJtwOOyqdhnGP3eBJKCjL6HU6WqoayN2VRfaWDLK3el5F+/MZHjUDp62VwCkrCb/+u4QtfZiUUamM9o3ofo5hQUGUNnkmNRv0Ruyunn8XHPYu9h9aT1F1ATWVdaxd8zeM6mqsOz4l7ZZHmBQ4iQfG34n25HYfXgZvEkaMpaW2nqSoJNIwsabsAKlGFzVdg7NtQ3hoKp2uTloaT/Rf+DJQqLRIjovbgFR9cnjYVX95hrhkWcLpbEar9b8s9zsfF9zTc/DgQV599VXGjBnT4/iTTz7JF198wapVq7BYLHz3u99lxYoV7N69+yw1QWFhIdOnT+eBBx7g2WefxWw2c+zYMfRnZAbtr163283SpUsJCQlhz549VFdXc++996LRaPjtb397oY8pXCC37EaSJZQKsUBQuHCu+gZUvr4oNAPbC+tKoVJpGTnp1h7HklrryD+8Bnd5Pq76YHZ2JnO4spWJYQGoTm7xYN2yGWt2LprEUaiMp//+tRVVoTVoWXDddLas2cO0+eORaMXlsjJ5wgOU7d2E3TkalVLDusIGfjHds1qmyeGi2ns4jZVF+AacMbk3MAny12JJXsmxgi96tb/OVom1SU9YZBDtjW0MG5+IRt97s8ia7DLcDlvfy9oBvVaDS3Yjudw4bHaONGfRkPkKUnYu8QGJqNVanJWd/O7O/4dao6E+qx1jUBxewZ79rbw1GtqKszCGDeuu0+jtjcNuZ2LMWD7e+zHfSp6Bn8mPFw+9yA8n/HBAc5j6MyX126zf+RyLpv/ssg0HnY05+kbaij/GJ/GeC65DdXJ42NVQD4kXtnHv+Whu3ouv75T+Cw6BCwp6Ojo6uOuuu3jttdf49a9/3X28tbWVN954g3fffZe5c+cC8NZbbzFixAj27dvH5Ml9L1v8yU9+wnXXXceLL77YfWzYsNM/5AOpd8OGDRw/fpxNmzYRHBxMSkoKv/rVr/jhD3/IL3/5S7Taod/d9VqSGpTKnqo9TA+fPtRNEa5iroaG7k+pVzsvSxCpsx9AkiSO7fkvjuwtdBlH4G0aD1hwFRegsnjj+/8epP3t3+Es9EMzbLRnP6utWYx+wLM4YMaC8Xz61jtEj+tg0uTHUSqV3D5tEf/bvZ4qbRJygKH7nv88XsXcEUnU1exi+FjPMWdXGy2FB9G3d+B9ljd0rbkNY3wUo6ae+/fX4BNBV0MZ3mGeN1IFkNthZXtTOx1uz1J3x/F6DlXtpKu9g0mhKVyfci8Hil9l/MIHUSqVlO3bR1t5OX5xcXiFJdJeerQ76DEERVGb4clVY+tqobJgDx0ttTgcnnaHllnIbtzP7OuXMlo5nk92bmD5zItfRKFQKJg85hvszXiFqeMfvuj6LobGOxKXrXcm6vOhDvD0uLgvw2RmSXIgSY4rYsuJvlxQCPvII4+wdOnS/9/efYe3VZ6NH/9qS5ZlyXvvJHb2TnD2IgNoIAkUKLSsshoohbeF0t/bwegbOumkUAopLaXQlJ1AQsjeceI4dpYT771tWba1dX5/OHFi4r1kx8/nunxd0dEZt6TY59Yz7odly5a12X78+HGcTmeb7cnJycTExHDoUPsFozweD1u2bGHMmDGsWLGCkJAQZs+ezUcffdSj8x46dIiJEycSGnq5CNSKFStoaGjg9OnLxbmuZLfbaWhoaPMj9A+jxkiUbxR7i/deNU1VELrLVV3d7+N5LtEq9TTZB389IrlczsR532TOipX4u0+z84vPKD12FsfZ86inz0KmUuP37R/jOLEfe+qX5H58mLjrL09CP755D7d8+5sonU1Y6lqGAGjVWlImTSDRaObrkYH8PKeETRfKuT7Cn5smR6KoOEPJ7r9TsvvvVKdvRRccR7lP35ZEqKttJjdfzYnzW9h58mN2nvyY7TlmMixWbg0L4H/iw/if+DDGJ41n1s2L0Bn0yC7+KdAHh1Fb3lI3JnDUKGou1pDR+Ufg+spSE025Ozm54y/kZn5BYHgykxfchymyZVq9X6g/E66bweeffIoBI/4+JnKLivr0ui4xmeLw1QVSXHx1zZ7hRq7VIjcYBmUGV23tAQIC5gz4dXqrxy097777LmlpaaSmpl71XHl5OWq1GpPJ1GZ7aGgo5eXt97dWVlbS2NjISy+9xIsvvsgvfvELtm7dytq1a9m1axcLFy7s1nnLy8vbJDyXnr/0XHs2bNjAc889152XLfRCnDEOg9pAelU6U0O8U/5cGN5cVVWoIiMG5NxJwTM5V3WQ6VGrBuT8XQmLncKclSYyD/0XRXkWVqMLnyue19/6KOaP/k5TTTCGmJZChmcOpRExJh6tTsv0+T/i1OFXiB69AmNgIkdr8vnm+JaW8GaXm/dyKrltdMuU6pKY1cxe9JWKuFlp3YrTZXNRe64Oa2Vzm2J5hwvrWHLLGIKDn2zddupYAV8P73j81aWjQxMmUJ6TiVTjRmf0w2pp+cJ5ZVeSrfwottzNJN3zC1SG9tdIAwgKCmWscQIBIUH4xRj479ZtxISHo1T2fZ7OpPG388W+FwkLnYxSpev6gAGi1AbiaixG6dv79eeUQUG4KvvWYtQVl6sJmVyFXK4Z0Ov0RY9aeoqKinjiiSf417/+1Wa8TV9cqvZ588038+STTzJlyhR++MMfctNNN/Hqq6/2yzU68uyzz2I2m1t/ivrpG4JwmZ/GD4fb4e0whGHKVV3V79PVL4nwG0uFxTtVai/xD41j6oK7KVDmg1ZD1YH/tHneeMu9+MdpaSwqwGqzcej8MQodWUBLi9GE675D0b6t/Of0p9wYfXn6+Oc51WyYGt/6uN3W1otDX1qnistl5GTu5+jHf0Umk9GY5aFgWz5lB0vRBmiJXhZN7Iq41h91vBGjseubmwyoKamiIr+cijNncTjtBITFYykvYvfJnVTsSm07Dsdtw3x0A66GPExznu8w4bE1XE7C4hbGU328Ao/Lw/L5c9m6d3+XcXXXwllPsOfo7/rtfL3hF7+Ohvye1Zb6qsGo1VNXd5CAITqW55IeJT3Hjx+nsrKSadOmoVQqUSqV7Nmzhz/84Q8olUpCQ0NxOBzU19e3Oa6iooKwsPaLOAUFBaFUKhk3rm1T69ixY1tnb4WFhXV53rCwsKtmc1163NG1NRoNfn5+bX6E/pVdl02EfmC+qQvXPndVNcrggUl6+jq9ub8YAsIYn3InF6ouoPALpnLnm22ej1m2lKqMs2zevJ27v3EPeh8Dh058AUDJrnQCE1dRXOZHYV3LzKgPsitZEmFCqWj/9Xk8HuoLC6nMLsD64cvUnDnIq+mvkhnuRJIkpt1wL7NueZjmRBmxK+KIXhKDX4zhqvcrOtSX3OK2QwIaXW7eyUvno/z9fJjX8lNa/wnluaVcf/9qJixbQVFFLnK5nJTbHkeREHMx4bmc9DTIsjFMfRzfMbcDYDfXUZN9mNL0TynesoWK1N0ENhrJ/vRES+Vra8uXqqhlMRRszcdP70tYWCAnz5/r/YdyBY3GQHzUXLLOfdgv5+sNudoXTx9nxCqDgwc06XE4alEqjUNiUdHO9Oi3funSpWRmZpKent76M2PGDO66667Wf6tUKnbsuLwgXFZWFoWFhaSktJ/9qdVqZs6cSVZWVpvt58+fJza2pWbF9OnTuzxvSkoKmZmZVFZWtu6zfft2/Pz8rkqohMFTa6sl2q/jpmlB6IinqQlPc/M1M5C5M3pjKFMW3kdWwQmUkeOp2PqnNs/HrVqOZEjF42xi5qTFOJ12Uvdtw+ORqCip5PvLF5Nd18CXuUXkWKzMjfAHoLCqiLyKfNwVFWR8+CEZH37I6U8+oa6wkBm3PYxuzZOE+S3kkSmP8O1ZjzFq0nyUKjUOpxO5vONZUG6bjcCmInLz237RtHk8aNyVrIyawZr4eayJn8dNoycTHC+hM+gZFTmO3LLLyciVBQidNgcFR85TY4uh/MwuSk9+SunJTyn/cgc+gXFETPkafn6TCR4/h9gFSwiaEo1KqaKupOVGXnaojMCJLf9XZoybSG5+MXZn/5TOSIhdQFlDEc0Nxf1yPm9QBg9sS09d/RFMppkDdv7+0qNOT4PBwIQJE9ps0+v1BAYGtm5/4IEHeOqppwgICMDPz4/HH3+clJSUNjO3kpOT2bBhA2vWrAHgBz/4AbfffjsLFixg8eLFbN26lU8//ZTdu3cDYDQauzzv8uXLGTduHN/85jf55S9/SXl5Of/7v//L+vXr0WiGbv+iIAjtc9XUAP1fmHCo0ur9mX79wxzf/ioTJq2k9MMNBC1+ALUpBJlczq3LfsInO37G6mXPs2DW1zj68VZKpHomz2+50dw6bjQ/2bKJG5PGALF4PB72Z2VQ42pG7gnkpmWL8DHouxVLeU0VASYjbreHwvPnURQXt+kik6uUBCfEk5+dBVxeVyxIrWKcfzTFlhJG+bfMwA0MG0fhhe2ERE0h0BhCXVMtJ89mkldfQrWlGld9IBqZi5xjWfhWyphzy/o2rUqNtjJUmABQR/hiL2pElxSAX2QAtvom6ipqaLpgJ2BsAH4X1w2z2m04HE6U8v6rv7tgxnq+2P8iK+f/pM3SG4NFa0rCVnkMbUjvCv4pBrB7q6EhE4PvuH4pFzDQ+r0i88svv4xcLmfdunXY7XZWrFjBK6+80mafrKwszObLsybWrFnDq6++yoYNG/jud79LUlIS77//PvPmzev2eRUKBZs3b+bRRx8lJSUFvV7PPffcw/PPP9/fL1HoJpfHRa2t1tthCMNUa2HCAWzpUchUONw21IqhsUCuWqNn0vxvce74Jgx2P8x/eYrwm7+Pb/xoPA4HY+2j+fDQVtbNvYGYKbEc/88H2CcnoA/0Iy9vLw9PSSTXbOfw+eNUNzSQHBeGR+FhdyNcuFDA5Gntt3p7PB7OmEs5Up1Ns8uOo8FGVFMS5Y0laOpOsuz6dcjbGRgsyz1/1bZoQywnyo+SYIzHgwetPgCn43I3mHT0BMX+Ptx4322UfVZA8Dcj0fm3vP8Ne4rA5QH15aRHHeWLo8iC0l+LOtKXxiNl6JIC0PhoySutxup2Y/DREh9nBKD80BkO7z/ALd+7B4Wi/7pa5HIFU8bfQdqJ15k27aF+O2936aNXUXPq971OepRBwXjMZjwOB/J+LOHi8Tiw28uH3HITHelz0nOpNeYSrVbLn//8Z/785z93eEx7g+ruv/9+7r///g6P6c55Y2Nj+eyzz7oOWhgUaRVpLIlZ4u0whGHqUvXYgUx6EgMnkl2Vyriw+QN2jZ7SG4OJiEuhMbCQgFkbOL1zJ/Jt24gak8CYG+6mITuT1HMnsB7dyo0/+H+kvfceKCXqzblMnXIvkZFw+PxxTHofyq153DD6ViYus3Fi30FgHObqOmpPbkZtbfniqbXWsDXrNQ40Gfj+xLX4a3zaxHPmbBiNNgt+vv4dxlxQ08TurCrS8mu4b2IEFns1uwq24XRbWTlqLVeOpJgYMROFKgKVUokiQIvsim40VageR0kT2nhj6zZlqJ7SwzlY60vwuD00p+cS3FSGTCGjQGnmm+tu4L/ppUBLwmOta2Tmmlmc2/shk5fe3g+fyGVhgUkUlh6juuQYQZGDu8TCpZXde+vS75G7uhp5RP+Ns6yp2UNg4MJ+O99AE2tvCQOm2dWMXtW95nRB+CpXdRWoVMiNxq537qX4wOl8cX7jkEp6AMITpnLqwGm0kS7mfvsenA4HJ7YeIPeTXciijViP7WPi3JZlKqbcdhsfvPN9Ft70dOvx142Zzpd5W5gXsRwAjVbLuXOFuKs+QWf0o0Djw5qbWir8RgKTAWf+gasSHoDA4DiqqnLx853eUtjwwhGMlUdQq1TUZLv4gyeeUD8NKyeEoQ3QUGd3ccPoWwHYmfd5p6/TN8oXS2ED2oktg9VVUb7kfnACRV7L3w1tgJ7IKfEUSdWkXLcQpVrFgeZi4m5oWb/LtHcfiotdYWU7U7E2u0m4qWXIQ9n5E1QVXyA4anRvPoIOzZp4F1v3vciKkInIVIM8dEImQ3K7kCl6fuu+1E3sqqpC1U9Jj81ejlodhFw+fIr/Do3pC8I1p6KpgmDdwMy6EUYGj8WCwmAY0HECCrkKSRqa6/ONS/kG2Se3AqBSq5m1ejEl9rOo1SqCFizHsus0Ho+HzCOnGDv22xzP3klFXRkAufV5+GkC8NO0jHF570IFY29bw9xvrGbajYvaTSS/2obwyvFM3s48y9aSRprP7Kboiz9TlbYZn9BEqtTRsOgZRo9O5rtLR3P7zBhC/bT4a1VUNjvanFNyu7BXnyVvy+ut25usTexPO87+3HTyzpzh2IF9ZJ44htJXjS1cRuKSCSQumYCtrgmAwMAAlOqOlyIpqm3m8NFcIpZdnrY/44Z7Ofvlf1vLovSneTPWsz/1D/1+3q74RiyhsfDqZUO6Q3FxdrLb0rd1vK7U0HASo3F41WATLT3CgMiozuD62Ou9HYYwjHnsdmTakTsJQS6Xo/UxYW2qQacPJDVjF1NnzGXsqCl8tmcvplnTuPDLf5MfG8zNdy5nPOPYnvZfkl0zOFt/jBtH3wZAZbOdkiY7t48OveoaHo+HYvMZiuvPIdG2tIdRreKuicktDyaPb/Ncfda+ln9IEpLH01pUMEinprK5ZcbU+bp8yptKKdh1B+NTXqI64wR5W17HUXqMU3I910c/Q/i0yzEdP9hyTmUX66zZ7A72HUqjprqWYrOZH+3OwmCw4JlVQs7bv8Sgk4i641nkCiWTV9/L0Y9e5bq1/buUhK/On4CwKeSf30LcmBv79dyd0QZNprFkB4ZeHCu7OKFHcvTPjLamplx8dHH9cq7BJJIeod+drj7N+MDxXe8oCJ2Q7A7k6pGb9AAkzVjL6cPvMHn+fRQWnWfdjQ8DsHL+PPa/uxP1zAmoXDo8Hg9yuZzrp93Ku0feIzTscomQV8+U8qOpsTTXV1KQdQJHcwPlVfvY4puFDDnhfglUNhUj9w3H7XGjkLcM/rXabF3GpzUGY66txBTUkjAZNEpezioh3XKYJaEJ3DXhARocDaiNCUQubFkEtToggAbJhd3VAFydiF1Jaa7CvuVvyBsVXNidDYCr5gxJKQuYn9LSqvOn4wWY9H44pFjGf3sdlvOpmE/twn/y9RgDwjGERZOXuZ/4if27DuD4hOv58tCviWyeg8qn4/FOQ0Vr0mPvr6TnAiEhfV/nbLCJ7i2hXzk9TiqaK4jwFQUJhb6R7PbWP9QDSYYMt8c94NfpDaVKg8fl5sjJHYwfO7t1u1wuZ8pNM1BqGpg1LZJPPs9u7ca5Y/btZNXCodI6/ptdwdKLhQqzD35I9NhZTF58G5MTx7Ni1APcMPZRpkauwFftR7BaS1Hj5SV7tO20snk8HsoKC6graxk4rDcFYam/vLRBpF7DXIOemQYls0JbvvjI9eG4LPmt++iiJxDhcpFTerb1nOWVJViKazHvKMBZ0dy6r0pzDrVPDcm338foRV9n9KKvE5k8ldDAy91zCf4+6JVy8ppbOugcjdUgXe4SHT/na+Tt24nVbu35B9CFhTMeZ0/aK13v2I/kSi3uXqwZd2nGlqcfkh6L5QwGw/CsfyeSHqFfHSwRK6sL/UNyDE7SE2UcQ35dxoBfp7fC46eSn/klyQlT2mz38zOhNxqw2SpZNDeaDzZn4Xa1JD4xRh1lTQ5yLbbWQoVyYwRKVcuNLzQwiYrKltdcVZ7HkeN7UHnqyLVcTno89fXkff45B175I/u3fML+LZ9w6PPN1FdVYtS3DHg2VxQSFB7XeoyPSoHD5UEpV2FztbQUqYKn4KhKB8BaVUhTbioGh5Xc8+fZcfgzdqduo6qugpSbV2BcGosq9PJgasnHp816XAB+obFUlVxePiRG5+JoWS5NMgNZRSeoeu1PKJSXz1F65AjRcdP46PBHPX3ru6RSaRgdu5jTZ9/v93N3xC9uLQ15H/T8QJWqZSC0ve/LAtnsZeh0w7PorOjeEvpNRVMFRo0RtWL4jOQXhi6P3d6v9UQ6Mjr4Ovbkvkti4NAckJnnzmTRzd9u97mxE6dweNdOrlscz4pFCby/OYsVKxI5Wd6AUy3nh1NjW/c1hsZRXZJH1KgJhIdOJjPrQyIjZxEcFk8M4ZCxjTM+c7Bm25EcDibt30PoY9/BWlPJuBtXt7luefUxJI+Hyppath8uA8paqytb1DJCfaPYnf85KoUG+YUGQvO2EZBTgcIQhHHi9Wj8Q1n++VFir5vV7uu61F0n1/jistegbKxqKQh4YTuhVTlkaScRFjuGlz/7D7MS4vmfackcOptGcXUeyTetxG9CyyrfZamp2GtrGX3DTTgKT7P39F4WjF/QD5/KZbHRc9h9+Dc47U2oNAM/W1WpD8Ntr+vxcTKZDJlG0+fuLZerEYXi6ll+w4VIeoR+IwYvC/1JsjsGpaVHq9Lj9gzNRXGrLfl4JBehhsQO94lNHs2ZjBOMmzSV1ctH8eg7+1mSMpoYox71FetvBUcmkLf5VaxZ+8Fpw6K73N0TFhDHzBWPcGkRAevpMzjnpOATHIxSrcbV3ITS5/INXWEIwl5bikmv5onFY9rE8/tj+Yz2j8ekDSBMH8In5UX4hIUybsXiNvt1VHFGa9CzPyOLYnsTfkX1zImNIuDwK6APgoSlaKbcSe2uL5DL5YwJC2b+uMvdfshAdnGyVnlaGs3l5SR+7WsAjI8Zz1sbf0ty+ChCAvq3+33u1EfYe+yPLJ37w349b3+TaTR9HshcX390WNXl+SqR9Aj9IqMqg4lBw6MipzA8DNaYnqEsreDfLBv3TKf7hIdHU5h1AYDqzHM8PyOcv5w4wZpblrbZT6vTU1qmIumBO5Hrjbi3vdrhOe05OWjHt4zZMMYnUHv2DLpAHbVZx6C5Fl1jLp7EGchpv5yAQq4gTB/S+ri9vSRJorqggpwLxeRZtfj7tFRlrnV5qDF4uGf6RD7XabE1ZMCyn3b6HrReR6YAhYLytDSaiotJXH25hWrPR++z8vrb2LX3v9y2+rF+XXBWpdETHzGLc+c/JXnM1/rtvB1eTxeG05yLypjQo+PkanWfxvR4PA6QyYf8oqKdEWN6hD5zup3UWGsI07e/mr0g9EbLmJ6R21Wamv8u4yK/1q2bc2hMNHkf7KRo9w6iJyfz3O2r+OijbdSbLW32c0ZORK7vutij/cIF1LGx7PzzP8k+nU/+rjex1ZUTteh2Yr/2GCEqMz5n/4O6uaTLc0kS2Kz1nDnyH9L3vkn63jc5uXcjJ6v342i2kTQzCU0ArFiayIqlidx+/Rjcaj0GjQaHTIHkdrZ73kpzJTWNTVdskSGTnFhyCmjML2iT8Oz9+AOSZ8wiNCqaBXNv4Yu973YZd08lxC+hsjabxsaKrnfuI0P8LTQUbu7xcS3dW71v1ayrO0yAf/uLhw8XoqVH6LPDZYfF4GWh33nsDpSDOGVdkqQhs2BiXXMpVkcdUf4Tut4ZiEsYzbHPPyJ8ynhqz50hIHkcd96+mn++tokb77yZ4AAdADapvvUYh9PG4X3/wdJUg4+2beUXuY8PcqUSbYCJOXd+jYzdCoKnLru8wx3/xON2U7N9E1UWO+crLNhdbmZEGCmpbOTzkyU4K5tRhfjgrN1PRHIUUWMmovUxtZ7C7fsBYUnRyOVyrI7LBSK/muR57I1UHN+My1KNw3mWgIAQdKoAzpf5s2522y4zZCoaZyYzesLlG/Pejz8gafpMQqNaBt6GB8dwwcfA+fwMxsRN6tb7213zZ36XbQf/j5Xzf9yv5/0qucoHydPzopp9GdMjSR48khO5fHi3voqkR+gzmUyGStF5QTFB6K5qazVOt7Nb3VseyYNc1vcGa6XPGN458w6hPibiTEkkmhK9mgCl5r7VZbfWV7nCfIhbcQOZf3uVgORx1BRXMnl0HLv3FDInJZKyxmz8tGeBNQDMnL4atUaHKSC8nbNJ1JRU4mxqvvio7XvhcTTw/gev888zBjJ2fwnAApQ4VozmmdkJBAb60LCjAL/JkZRhIHzy1ct8+PiYaGysws+vpVZPo8PF3uI6LtQ24XRfHvFjGDUHtTEEn8BIMjPfJX7iHcy+6mygVamIDBqLRVdBcfERGpuKaSzyZ/zMea0JzyXzZtzIpv/8gPion6NS9t9iszKFghljbiE1/e/MnHJvv523v8g0ajy9HNNTX38Uf1P7A8+HE5H0CIIwZEiSxLGKY6jkKmJt1k6THpfHxabzm5gWMo2kgKQ+XXda6HV85jjGktj5nK09x0fZmzEo5SjlKiaFziJAa+rT+XsireB9Rodf3+MxJwGxSRSdP44pPpGq9HTOni5h4V03MhV4/6OzePwrSDFc7toKCe94PEi6LoyQs9nMuK2l2rDDXEnatg0kBrV0N8qUOjIb6jD7jOMvt03BI8E/3jlJcII/gYFtZ/YoNQYcTfWo9aY220NDx3A87RPO2WsobY7m013VPDNlJivjAtu8dlPC5Vl17S1WfUlscDgOp5MaSznLp9+G02ln19kXCI3+BtAyI6yi4Djh8TNJ2/EqSxY/yJ7zf2LZuO93/sb2UFDoRIrKjlFeeYqwkO611PWGLmAyzWX78Qnvfiu7XN277i2rtQhJcqNU9qYW9NAikh6hz2QdDGYUhJ46VHqIlPAU/NR+ZDb/DEMn6y3tKtrF2tFr2V+yn1GmUa2VhHsjSKunwWVDLpczPmgc44NaBvHaXDbSylNpdjYgA4y6EKaGTOvTtTpjsVXTYC1lWuy6Hh8bM3Yuh//9B6bfeh+f/uKvrH7midbn4k1FTJqzhPKD2a3TwTvjNpiYvGxO6+OZNz/Kp8d2MG16y+Bol9tD2vbfsHxiLKsmhmO2OnkLKK23Mj22pS6QJJNRl1OM+XwTRaWvYIxaSOKYua3nlMkg2xmOQ6tCqfIwJ8qHKaF+ncal0RhpbKxGLVfTUJyJw1p7+TmXmxy5ieigUWSVnCYpcjxRYybTbKlAqw/m2PY/o1RryDn5JRPm3oEpOJ54qkkr/IRpMas7uWrPTZ1yH9v2PU9wYDKKXiwO2h0+kYupyfxdj5Ke3nRvNTZm4XSaCQiY2/XOw4BIegRBGBLKm8rxUflg1LS0RmjdCgpsZe0uVHCi8gTjAsehUWhICU9hT/EelsQs6feYtEotc6Iud82UWErZXfAFEh5kQHLQFCINkf12vcM5b7Bk7P/06tgdqZ8R5jFwcts7jL77Zorrq0g2GSjIyiAkIgy5QoHaEI69pgBdcHyf4jxZ3FIRePuZCoJ8NRSWlpJEMaa0RnLSrBTWWYhIGIe+PIBadzNT53+frZ/+DktzDh7JhUwmR+8Tho8ikgcX3sQrRzbTXk1sD7AvJ5/yejN2l5OZUWM4fWITftZAEq5bjMbv8qLGZSc3c67gJBp5HHptANml50iacTNHP/8jKo2W5Jk34xcQg91qQaNrabFIDJ3HgfOvUtFYQKhvbDsR9N6CqQ+z79ifWDT7e/163ku+WrSxW8do1D2asm42pyGTqfD3H/7dWpeIpEfoM6nDihuC0D2SJJFelc7KuJWXNzocBJsiSS1PZWbYzNbNpY0tSyBE+rYkGz4qH0b7jya9Mp0pIVMGNM5IQwSRhpYaL26Pm/TKE5ytTkcG6JS+TAufhVap69W5M4o2Exc8D4W8d3+WZ0+az1l9Bs2OZuaNn8g7720jPiycuooKfNwnsAUE4BsxkcaSzC6Tngbn1b/Tcpkcl9uFUqFEKZcxTpZPRa2TtB2H8fMPRjt1ApFLJiA7+i6G4HBq7eXUlpURPW4CKpWaAP1EpkxZhdvtam39OFaSDoBMJkdeb+Gz3QeQZDJKqmupGTcBg95IqEHN3PgYzHY7h3ILWDnnYU5/eZyyzGrcjgrMBbmExEWDrxu1yogM0KjUlNUVoFHpSJqxBqVGiV9ADEBrwnPJnNEP8cWpDVw/4dl+GR92ic43lEhTAhfydjI6vv8Tcmh53yS3A1k3C8LKNZpuTVmXJIma2j3otDHo9T2bFj/UiaRH6BdDaeaLMPwcKD3A3Ii2zeeS3Y6fIRCrSk92XTaj/EfhcDs4XXP6qiKY0YZoam21FDYUEuMX06sYetpNq5ArmB42o/Vxnc3M4ZL9uD0OJCDGOIrR/mO69XthczZSZTnP0uinehp2q6CAEGxZdkx6f/751qckxyWz77+7mTBdh0Z7PU01eewqz2J0vYrgKZ2fSyeHc+eqKCppwNXQhMdSQpOrnMaQIPwixxMnFXHnhACCZn6PSJMOufzya8yXw4xVazo8d3vdPTHGEFS5Zxg7egrRoyZRmFdAk8XC2LEtY2Jcbjcny6vIPX+ePZVlBCkNhE+OQ+Or48CWXAInj6LoWBHnm32ZH7GYScF+pF3YS9qFL7lh1jdRKTtOCmQyOaNC5pNZup3Jkf27gObopNXsPvgLoiJmoNN03nXXG77RK7Hkf4xf4m3d2t9ZXoGztLTTfRyOamrrDhEYMB+VytQPUQ4tIukR+izCN4KSxhKiDFHeDkUYhkobSzGoDRjUbb+BS04nMqWKcYHjOFByAJPWxLGKYyyLWdbueSYHT2ZX4S78tf5Xnas7ArVGiptqiNIH9up1+GuNLLqYjHk8HrLqLrAz/zMkQCVXMSV0NkZt+zVyDpx/lXlJ3+nVda+0eOYKPt33X1LCUzCG+JJVZUZyShiTFlCR+RnBpmCspZdbMzweDx63B6Wq7a0g3q5Aq1GyZGE87soa7OfdaKfPouTgqzTm7kVnjCGxIRi/gKuXI5AuVkS2VlRQc+I41tJSaht9aA4LYev7e5g8cyzhMSFtjpkYGsv+mhJqC84RmTABX7vE5iNpHKNl7JRMBrPCQ1m05uqxNzK5DJ2/gTHXr2Z26nF2FJXxn3MlrEoYx5Ip09h67F+snPENVMr2B8V7PB6yK/cwKnRgqskvmPkE24/8hhXz/l+/n1vjn4yl8LNu72/LzOz0+fr6Y3g8DsJCB77AoreIpEfoswZ7A/HGvo0REEYmj+QhszqTFXFXf8OWqdVIzpbCdHMj5/J53ufMCJ2BspPun0XRi/gs7zNWxa/qcVfF3NAk9lec4Y74OV3v3AW5XM7YwCTGBrbMKmt2WkmrSMXutCABgboQJodMQy5XcKb0SyICpqFR9n09I6VSyfUzb+TLY1uYYVlAXHQyoRMmIZfLcTosyD1gq3Fy4J1PkMlkOCxN6EODmHlz20Qy0keOf3zLgGRZWCBNh86hMgQSt6Llxu04fRgp0bfdGJqbDex/egNRs0YROHMmEctX4tm0j9lfX4jH4+FcRh455wqRyWTUV5r5zxc5LTOrZE0kF9rJL/scXWQYQQp/pip8GD3+8libU6c2MWFC21YNyXO5K04GfHNcJHce/ZLS7BpqLJCo8KfhyC8YFz2ZZkcVMpmaAN94VHIN9c2FVFuyuC7xIYw+7Y0e6zu5Ssvk2CWknfkP08Z9fUCu0V266dNxlZdftd3ttlJdvROjcSpabf8u0THUiKRH6LMGR0Pr4FNB6ImDpQev6ta65KszTVbFr+ryfDKZjCUxS9hZuJNlse23CHUk2sdEja2xR8d0l49Kx7yoywtdFlqK2VWwDQmJo0Wfcevk/itm5+trYNmMG0k9c5DFsy8nk/6R0yg77WL2Y+Nbt6V+/CVjF3Y+SFUul7eUVb6C88xRtDfci7WunJq8U+TlllFeYyXE4Et8SCjGxVH4T05CHxEEXJ5qLpfLGTfl8jpibT/50W0e3bpgKod2p7dJeszmC+Tk7iI+biEORxNWay1NTcWcO/FP3E4bNfWlvFNg47eTrmOiKYyyZjM/OrWf8OivE6GXY9Qtxe2xUWXJxepsINw4numxt3bnbe2TsOgUilLTqK7PJ8gU16/nlqv0uK01KHRdt1AqA/yR69sm142NWVhtxYSErBzWy0t0l0h6BEHwioqmCvQqPb7q9lsMers4ok6pY1zgOI6VH2PGFWNuhpIYQxQxF7uDF8au5NOidLaVpOMBJgXEsDhsXJ/O7+trwO1pOx/KEJGEf20F+z45z/zVLYuEOizN+Jo6HmsiSRL1245QmZfJ7r9uo7qukuDAMOqbHcw9tg2tr5HAuPGcK9Kz5oFZKJUtN83Kg5k46i2tSY9Kp8FeZ0Hjf7nb0W21Ysk+jb28AC6OCZIhazMxwlSxmXNbrDiiW7q1/P3H4nG7OHXqPVQqHzRaE+Oum02j5UvGTPgORTnvsDJpLln1pXxUeJwaezNz/Y2UWevItdiI0du4LiSJuKCBn43k8XjYU3EeH6Wa2cEJzJz+KFv3Pc/yBT/p1wHTxvh1NOS9j/+4h7qOyW5HfrH2lSS5qa7eiU4XS3DQ0i6OvHaIpEfoE5fHhVIm/hsJPSNJEieqTrSdrfUVfVkcMcI3gjpbHbnmXBJ6sCijN2YiquQK1sZOb318qDKH351uGaexOHwikwOiOzq0Ux7P1ZPAZcpqjCFajnyZi0wuo6jsHIc/KCEwdgwRSdOoKM+hKvc0+mofcg7tA6cb+YXjnI12MmbcMlYlzSYr7ySnv7hAlT0Jk68v0YGR6HwrWhMeAJXJF2f95VYzY3wolSdy0NbvxmOIBrkMuUqDLjYZ4/hpHU6/DnHfRFPORyj94tCGdZyo1NbaOXb2p+Q0q/nRsXeYGTyWiQHRhGn0gESz242PUst5SxXv5u6l2eUkUBfA7bEzUSv6r3XjVH0pu8tOtf4/mhU0igOVZ5kdnAByOfMn3cf+Y39mwczH++2aCl0Qbmf3WigluwOZwQ+rtYQGy0mCApegUPRfRerhQNythD45V3uO5MBkb4chDDNHy48yq5ObGPR9ccTxQePZW7yXQG3gsOp+TQlJJCUkEY/Hw2clGewqy0QlV3Jb3HWE6Lo3A6iwJI9QU9sFgAtys3G5XTQ25jJz4SKqqqvQ+i9nwuRJlOVmkLVvM4EhMUyadRMfWD/BVneeZTOXEbtgEaVfvsXMSS3rXE1OTsF+XsbY2YlcyCxq9/pqk4GmA7uw5G1HJpORkxfPjO/fSPXeHEKv737hRZlCjU/8TTSefrPTpCcgYA5qlxFl+VHWRKQwM6jtGMNLbYnTAqKYFtDSwlbcWMWrWdtxSW5ujb2OGN+eD2Avt5r5tDANm6fl/2m8byiPJC1FeUXxymPVOa3/1vvHEuIXS27uDhISBr91RbLbcVCPw1FBaMgNg379oUAkPUKfVDVXMSFo4EqtC9eeels9EhIB2oBO9+vL4oiXzI+cz9b8rVwfe32nA6AvMaj0VNksBGsHt9z+/joLh+oaUV7s5nE2NjHl1AmuW7qIGyInYfO4eDfvEKXWBgwqA4+OmYe6k0q/Z/MzWTG3pUvI4/ZQVlJEWUEB1y1eii3RyolDB5DJ5UxPmYdcLidy1BQiR00B4KPDH7FkwUoCDYFsPrKZo5mHCdUEXXWN4txKIuNbigMq5HJcThdKlRJHbQUNe/6JMWE0vovvxXX2FMkJcgq/KERpsfb4vZGrfJDKTlAj+11rNxhtWuRkgMS56jKWTPseo43trSV2tSjfYL47biUuj5t38g5RW2DBV6XipqjplNmaaHQ2cdZcik6pJ1TnR6TORLBGz5dlmdTYGgDw1xi4IyEFg6rj1hIPUpsq2MlJq9lz+LcEBo3B6Ne7VryvUvtGY687h8a/4y+gTmc9jqZK9PrZGI3T+uW6w5FIeoReK2ssw1/r7+0whGHmSPkRlscu73K/nlaPbfccFwc27yjc0e4Msa+aGzqGAxVZ3BI7eGOBPq6so87h4gcJl2/WlVXVXLAmUpSdQ1ZqGoZ8N6bRibiSxnKgpoLXsr7ELXkY7x/D9RFXf+lQKVuW7/B4PBzZuQNZk8SkFS3DhrVaHX7+/lTnlSJXtO1WSj2fSnhAOOH+LbGsnbuW1D27GDv58k3S3mxHqVZSX2UhaXIs58vOc9KRyrGtx7lh+jKifI2oYidjmNMyBdxVUo5x+XKMgH1XRrfeE5vdiUohR3Gxy8wYeB0k3gK+wR0eo93/Cw7WlHY76blEKVfwrcSWpRyq7Va2lpwgQO2DUWPg7oQUmhzNlFjrKWgsI62micVh44nS9+zv3leX/Vgw63ts3fc8K+f/pFeVlb/KEHcztadf6TDpaWjIwOmsR+HRouxma+G1SiQ9Qq/lmfOYHd7eeseC0L70ynQmB0/uVsE+uVqDpw/dW5doFBqmBE/hSNmRLv+/JvoGs604vc/X7I70hmY+rapjltGXe6OC8Xg8HNu1D4fNhuRyEldbTfR99wJQ99+9lOrU3B3iT63LzerwMcTqNBypyr04/kfGysgpJJvCaWy0tF4j59wZYiYkERnetmBj8sTJNMVfPdapsKKQdfPbdj811Nbg63e5e7Akq4iwxDCKCuvZ9tkBstQZfPf6R3F7PPxj938wagyM9lQRBLgK81GEXm4l6m790nNZZziTlUVEkKllQ7Mv7N8DXynwV2e2kDJzOmGRcYSodZQ4+5YkB2l03J3QtmSBj1JLsE8AU3p5Tnk7RS9lcjlzpn6bA6l/Yt7s7/byzFecT6FGulQg6QqSJFFTsxudLhY/v0nU2V/odBHfkUAkPUKvefAM2MKLwrWn2dlMg6Oh20tFtMze6nvSAxCqD6XOXseFuguM9h/d4X6DVVU8u9nGF9VmfpwYiSRJ7PjtH9CMGUPEmFEkjBkFQNVf/nJlYCxLiWH/vnzWXxfNhzVmKuy1yGQ+PDB6OUalnI+KTrC1JJ1Ky1lim9WEnk+nucbM6HHtdz/rfa/uwlMAJceOYa6owDcoGGNkBCqtDofNxoUjBygur6CmXuLr999OxOgo9m+6QLTMjlwuRy6XMz1uMuPjkjiS1hJ7+r4sTFOnMerSBTR6PI0NyH3bJi9uaxVNJTtxNLdUC/a3u4kePYv5UxbQGafDzpHD+wiLjANk3c+qBlX7MRn9oggwxZCXv4f4uIX9ciXJ42ltOXK77VRVbSMwcEFrZWXJbkem6d6SFdcqkfQIgjAoDpQeYGlM9wdvyjQaPBZL1zt2U3JAMgdLDhKgDSCwk5omgzF/6x8lNTw3qqUInEwmQ5uUxNwbO+l+k7UMZ7luTgyZpyr51rSWY5tdHjaWVNHk9uCrjGJK2Gj+VJHFwrhR7Cncy5KgRZ3G4bA2c+yT11GoWm6EkQo9mmQDY8aPx1xaSk1uLg1HDpLusBI/dQZ1Nge+QaGtFZw1PkrUPkq2l53j+vBkpoy6XAOoob4GV7wGj0fi4OfZTF8cB/5hmE9uxGOQ2syUU6j80Icvws/UUsMnCMg4tqPL91Gl1uByt8xSk/BcVU9oKPBwdQvMJeOSbmHHgQ1Ehc9ApdH36To+IbNoLt2NPmoJNlsp5oZ0QkJuQH7FWDaPw9E6ZX2kEkmPIAgD7mzNWcb4j+lRfRK5Ro27um/dFV81J3IOW/O2sjR2KSq5ql/P3V3bqs0sDfTrUauSKtiINf0C+mlJuD2Xb6I+SjnrY1sqCdc5XJxqbGZGUCKVqa+ydMzNhEd1PmvNUleOMSqB8XOvXnYgMDERudHEucIS5ixdiY9CgSo7B5fUMjj6xP4smsxyan38OVdykvnBiWgvjiWSyWSkHjvK4kXLkSsV+Bkkzv/lQyITQzD7lBOX8vN+GctypSBjLJam8jaDhocCl8fVaUwLZn6Xval/YOncZ/t0HV3YXKoyfkmzxoNaHdju7CzJbkemFkmPIPSK0+30dgjCMHCg5AB2t50lMT1baVqm1uDpp+6tKy2NWcqOgh2sjG+/RpBOqabBacVP1bvV0ruS3tDMMwk9G2zrM3cC9f/ZiyYhAmVlDdB2nbsai52MvDrSzEcwBOlISFiGISiJC/t/wcTrf4Fa0/5sNI/bBYqOE4ST2XmsmDuH0yVlBDaaiR0zhtS92RzckUZzvZ3pS8cT5pIRUKfns5KM1npDTU3N7DxVj1q1k1mz53Fh1wHmPrEOuVxO855T3U54XC43+YV5lBTm4/G4L/ZetU0W1aqWRCshZhGqYxs5bS5nov/QWUphReQ0Pig8xq1x7U+5d8nVqLRBNFvK8DH07P/FlWTV59FqwvENXIi8nYRekqSL3Vsi6RGEHrO77egG6KYgXDucHicuj4tF0Yt6fGx/TFlvj0qhYkbYDA6WHGRO5NXrbM0MHMWhymxWRE7s92sDNLquLhr4Vc7qelxl9a2P5XI52uRIGvaepDD7CEW+ZpqtVu5asYKaOisf7sxn9bI4bOn+rBq7gAOp54mIngOSRHV5OhGx89u9jrupCZmn4y6hJruDMI0KldtNRVkpyVOn8bVvRON2u9jyzi/xD07BH8gqaKDYmcPpikOUl9vxNSTy8Bg34TOu4+DBvUjph7CuWY7e2H71bcnjwV6bSXPlQTwuKyDDZDFwaL+buIhQ5s+Zj7yLFgqtzsRklYzDNcVDKukZZwrni5ITbbbtKc/ieHU2GoUah8eB3icG7dm9zJp1e+8ukrsHtH74JX+r432cTpCkET+mp09tgC+99BIymYzvfe97rdtsNhvr168nMDAQX19f1q1bR0VFRafnuffee5HJZG1+Vq68/C1s9+7dVz1/6Sc1NRWA/Pz8dp8/fPhwX16i0IHMqkwmBU3ydhjCECZJEtvzt5MSkdKrsvsyjXpAkh6AIF0QQT5BnKs9d9VzE0wRZDeUDch1AYyqq79ruhpd1P77M2r//Rk172yh+UgGUtwEMnd/yOEPXuHwB6+QkbudbM9p7OFubp43jwCTPxarlUB/HSERvoQYL38JkSQJyeMhKGwy5qrTHcYiUyg6/eZfh4zT5ZVMjI5ErVK1dtGkp7/B+JmLObnvy9Ztc8pPM2bKfUzQJzBn8To0TVVotD40eVQovraMY5u2UJZTiNNWS925N6nO+F3rT+2pP+BqLMSUeBdBk54iaNKTzF/8beYvWk70mMldJjyX+MjlWN2ubu07WFweDzKZHJfHzd/O7+L/Tn6I3ePkqQk3sn7s9Tw5/kYeGrsCVW/WvXI54NQHEDQGIqZ2uuulVlO5dmRVYP6qXrf0pKam8tprrzFpUtsb35NPPsmWLVvYtGkTRqORxx57jLVr13LgwIFOz7dy5Uo2btzY+lhzxS/inDlzKCtr+0foxz/+MTt27GDGjLb1NL788kvGj788mC4wsOdVNoWu1dvr8VH1fVVo4dr1ZeGXLIhagFrRu2+Wco0GTx/r9HRmjP8YDpcdprK5khCfkMvXlcvxDOBwZpUcbG4P2iu6lVzjp+KbFIpadfnGl31kD4H+RiYuWtPm+Kod/0QulxMbHkZuWRmTE66cet7S9eNviKCuPpeAgFG4XbYOY5Hk8qvGFlUeO0rFyXQUGg0avR9WXTR6jab1PWkZn6ImMTmFU3W7AZDLQRk6HZVajwyo3vI3lMExyBQKfBUuFi5uGaSd+uF2XOWNxMy/CcUV73l/GYpzt+ocTWwv3INcJuPrcSmEdlAnJyBqMgV5x4mNn97u81dpKIPCQ5B8Eyi7/h279AVCjOnphcbGRu666y5ef/11XnzxxdbtZrOZN954g3feeYclS1r67zdu3MjYsWM5fPgw1113XYfn1Gg0hIWFtfucWq1u85zT6eTjjz/m8ccfv+oXNjAwsMPzCP1HqxzZ3xaEzu0r3sfUkKkdLibaHTK1Bsk2cEkPwHXh17E1fyuLoxejUVy+GbRXW6W/jNZpOdtoZarx8mydCKMPxdVNJIRfviHGJCRRW1V11fEymQy320V8WCg7j6UxOSGBJquTo+er0WrVvPvp56RMHUNZxUkCAkZddfyVJJkE7ssDow//7tfEzJjFxAdaFq+8ctlTh6NlDJ/FUo6v78UxRbKWJMhpa0ZSODi+7xPCa0tQBEQQuqhlcLQkSVgbLTSYa9HEGtCYZnaZ8OTk7aSpsYLk0Tei1l6dJDSYC8m48ClTx92B3ifwYnzNNNsbhty09WCtgc0rN3S5X2z4aNJS3+9e0lN6AmxmmLC223FcTnpGdvdWr5Ke9evXc+ONN7Js2bI2Sc/x48dxOp0sW7asdVtycjIxMTEcOnSo06Rn9+7dhISE4O/vz5IlS3jxxRc7bKX55JNPqKmp4b777rvqudWrV2Oz2RgzZgxPP/00q1ev7vCadrsd+xXN5w0NDZ2+bqFFg6OhzQ1CEK50rPwYcX5xBOmuXrqgJ+R+BtwWS5vaIwNhWcwyvsj/glXxqwalTk+KyZd3y2vaJD2xwXo+zighLtS3tbsoMDCEwgvZbY6tsdZw3l3I2LIcTh9Io9IvgH+npjFvUhJHz1SzbsFscooKqbVYaLK01LyRIaOoNoPcqn2t55HJ5DRaS0gKupGa7FMcKc5FkiTKnDUYIwyES9JV74XB15et2z4gKlpOdGRLC3twZDRnjuzFpjIzbvJNvHuiiMA507Ad3U7opdcWFU1GRhp+BgMlFZVEGzTYrE1odVdP0d535Pc43XYKa89xy7yfsif19+i0pstxSy0xldSe46aFz/Pm548wJaplvJJaoWbymJu5YG7C6Xah6mSZjqFKodJhszWh1XYwfV2S4Pw2CIjvsjvrq9xmc8s1jKIic4+8++67pKWltY6luVJ5eTlqtRqTydRme2hoKOXl5R2ec+XKlaxdu5b4+HhycnL40Y9+xKpVqzh06BCKdlbAfeONN1ixYgVRUZdnMPj6+vKb3/yGuXPnIpfLef/997nlllv46KOPOkx8NmzYwHPPPdfNVy5colPocHrEzC3haqdrTuOn8SO6H9YUUgYFgdOJ22xG6T9wy50o5UrmRMxhX8k+FkRdKoY3cMmPv1pJhd2Fy+NBeTHB8dGqWDYmlE/TS4gwaJk5Ohi5XE5jZR2fb/0Em9HN6erTxCfGM3fUYgqrc/E3mbhpxQreT8sgOkjPSU0tTreHqNAwduUXoFd7MJ9/G0kqoq65iHmjH2lTTLSmqYhD519h5k3fJtTQUh/H4/Fw+vxRNn/5FpLkQa5WERIWQ2N9HfXWSqJikikwn6Na/hHyCgUgIQVIGFwyXstLQ+unpNpsx2OsbL1OwphxJIxp+bfT5QS3naribPwjwikpPUZFbRYymRIZoFXpSZn2MMqLa1ldP//Hnb6Xj639z1XbJsvyOFpbyNzgqytOD3VJ45aQdfoLJk/9yj3L44aKU1B9AUYtA52px+d2VVcDF3+vRrAeJT1FRUU88cQTbN++HW0/Doa64447Wv89ceJEJk2aRGJiIrt372bp0rbFzIqLi9m2bRv/+U/b/+xBQUE89dRTrY9nzpxJaWkpv/rVrzpMep599tk2xzQ0NBAd3T8LwF3LVAoVnnZKngsjW259Li6Pi/GB47veuRuUQS3rLLmrqwc06QEwaU1E+UZxuvo0o/2TUAxwnZf1sSHclZGHTiHjl0lRhKjVBJt03DwtmsLKRv5xKI+vT49m4S1f48yuNMalTENxQsHi+MXoNXo+2v8PQlvbUlpMiA/gkwNF6LUyKqtOMCayBF3wSiaMubvdGAL10YyNugVzc2lr0iOXy5mYfB0Tk1ta5Z12G0WlOYxKTibAdOl6nVdJbnQ0kl4mkbn3YyS3k6aik/hETUQmk2NzudlqjkEm+xNfa55PeOgkxiSu7NeWvFmB0byec2xYJj1atRa3ywluJ5RlQHNNyxMyOYSOh/DJvT63q6ol6VGM8HGuPUp6jh8/TmVlJdOmXV58zu12s3fvXv70pz+xbds2HA4H9fX1bVp7KioqejTOJiEhgaCgILKzs69KejZu3EhgYGCn3VaXzJ49m+3bt3f4vEajaTNgWugeq8vaq9k4wrWrrLGMSmsl14V33IXdU8rglm+krupqNKM7XjqivySYEkgtT+VwxSmifAb2xhCsVvHmhDi+lZnHW8U1rdtnmvQsCvEjoKQerVpJs6Wp9bmk0CTOlp5lVuKsNtWML1HpzBiiTiLP03LbyvvQ+XS9UrzTY0Oj7HhCgkqjJSH+6iQ2u7GeY7XFVNksSFxa57yFTqGlON/Emthmpiy7k5LcsYRGj0Z5serzLOCVIzVMmXxTl/H1hlqhHNCB6AOl0u4ks9GKMyAZT95e5OGTIaqbg5q7wVVdjcJoRC7G9HTf0qVLyczMbLPtvvvuIzk5mWeeeYbo6GhUKhU7duxg3bqWReuysrIoLCwkJSWl29cpLi6mpqaG8PC2hZokSWLjxo1861vfQqXquppqenr6VecQ+u5o2dF265sII1O9rZ6suqxe1eLpjPLiN9JLzfKDYWbYTF4++U9WxV0/4NfSKxW8P/XyQGNJkthR08ALOSXENbVML07fcRSdVkdBbQF7c/bip/HDIKmorCpGVt3Ero/fQK5XsT23EJMuhGVxNyFP6N4XkoyizVRZzjE78f5O9/N4PGwrv0CGuQIkaHA50cgl7oubTJR+fLvjoP5QF8CUGbEARCb0T8vftcblkTjbZKXC0TLFPlitZHGAAXngeKD/3zNXdRWK4JHdtQU9THoMBgMTJrRdvE6v1xMYGNi6/YEHHuCpp54iICAAPz8/Hn/8cVJSUtoMYk5OTmbDhg2sWbOGxsZGnnvuOdatW0dYWBg5OTk8/fTTjBo1ihUr2q5Fs3PnTvLy8vj2t799VWxvvfUWarWaqVNbBnd98MEHvPnmm/ztb3/ryUsUukEmk3mthL8wtEiSxKGyQ6yMa7+6cV/IfXyQ6/WtzfKD5brYNcT4Dn45BplMxrIgI8uCjGS6qti3Mxc/XwOTl82g0lxJrH8sy8Yt4/Utv2TZxK8RaAim6Px2Fi/spCBdJ6xOM7MS7sVXE9Bme6W9mQ+KTnHaXEuirx+fV5TwnYSJhGn98FPIWRMz9OtzSUNgDS5Jkqh0uCi0ObBcUZDyUquYQiYjSa9lomFw/q+5q6tbu4xHsn4f3v7yyy8jl8tZt24ddrudFStW8Morr7TZJysrC/OlkeQKBRkZGbz11lvU19cTERHB8uXLeeGFF67qenrjjTeYM2cOycnJ7V77hRdeoKCgAKVSSXJyMu+99x633nprf7/EEU90bQmX7C/Zz7zIeQM260kZFISrnWnbA8nu8aCVe3fa8/jRgZQH6jj7aTFNtQ2EBIRw9thZ6prrsCokRo+ZAkB5gRa71YJG13VX1iU1zdU0uR3k2LVsz9qHWh2EhER+UxNJBiObysrZNncV96R+wR+nz2F5aCkWl5ubo9r/uzsU6RRKLE4bBtXAl9YosNrJab66tIIMCNGoSNJr8VP2ovBgP3NVVqEU5Vz6nvTs3r27zWOtVsuf//xn/vznP3d4zJVZuE6nY9u2bd261jvvvNPhc/fccw/33HNPt84jCELf5ZvzCdQFYlB3/4bbU4rgoEHt3oKWWcFyL9d6kcvlRAT5kh0WgLmiDn2AH08sfQKPx8O+vV9QUVJMaGQUcqUKt7v99cksjkYyanKpsjcAMjweD7VOGwmGYN4rusAvp6zir7npJOpbFiW1uiW+lzSHmf55+Kp0/DelZdzkONPQWdKhu64LimZfZR43RI7t93NX2p2carTioeVeFqvTsDjAMCjlDvrCVV2N9is9NSPR8CtkIHid1WX1dgiClzndTrLrs1kWu6zrnftAGRyMq3pwW3qGEm2wkZqyKgo0ErlV1fhpdYTGT2DX7l2EjZ2ArbgMe2w5jqYySqz1SJJEfpOFOL0fFbYmVsdMYY7PxKtuyEsipgDwg7HzWrddKnM3NyQeYMjfxDsz3i+UXRV5/XKuUpuDs0221qHRIWolCwMMKIbZ++OqrkYZLLq3RNIj9EhqeSpTQ3pWFEu49uwu3t3vA5fbowwKxpGd3fWO/cj7o0EuCwr1Y8+pLFImxnJnXAzNLjcXKqrQjZ1McmIc7184R6LaRH1TJatj5gASOeYSRvvHeDv0TmmVKiw2G4YBWgdK3ocp8IVWO+ev6K4K16haBhgPsyTnSh6rFU9jY+uMyJFMJD1Cj1hd1j5X2hWGt1PVp0jyTxqUwewtY3oGt3trKIkM1TN34TTGBLcMNvZVy5kaHcHU6JYup/W33wnAaGNk6zFDI+HpPHVMDIjgbFUps6K9W0tHkiQKbA6yr0hyorQqlg6D7qqecNW0lEUY6YUJQSQ9Qg9YHBb0qg7KowsjgsVhod5ez4SgwRkboAwKwl1fj+RwDNqaQUPpVqdRKxkTH9D1jkNO5+/i+JBotl3IGNCkp72kRZIkcqx28q2O1ghjdeprLsn5qkuTARQi6RFJj9B9Z2vOMil46E9XFQbOwdKDLI9dPmjXay1QWFuLSsw8uWYE+hhpsFsG9BomlYZyawP1HhXFNmdrkpPoo7nmk5yval2CQozpEUmP0H0Oj0Osrj6C7S3ey/TQ6YN6s7jUHO+qqh60pGcojekZvjp/F2UyWbtVpft8VUkiuz6b8qZyAnBwoK6RWQFBLAvU9fu1hhN3dTUolSiMRm+H4nUi6RG6Tay3NXIdKDnAGP8xgz6e61Jz/EiewSV0TJIkcs25lDZeXFFeJiPBmMD8iyuvCy1cVdUoAwP7dY2z4UokPUK3ON1OlDLx32UkSq9MJ84YR5h+8LuXlAEBIJMNaoHCkdPpMZC6fhdlvXinbS4bWXVZWBwWJElCJpMRb4wXSU4XXFVVYhDzReIuJnRLRnUGE4MnejsMYZC5PW7qbHVMCZnilevLlEqUwcE4y8oG7Zqie6s/9P1ddHlc5NTnUNlc2bpNq9Qyxn8MRo3opukJZ1kZytBQb4cxJIikR+iWJmfTgFbeFYamw2WHmR0+26sxqOPjceT2T6E5YQj5ytgwi8PCmZozOC5WmFbIFSQaE0kKSPJGdNcUR24ufjes8nYYQ4JIegRBaJfNZcMjefBRDf7im1fSJCbQnJo6aNcT3Vv9oet30enysK/4ABJuZMjwVfsyJWQKGoWmy2OF7vM0N+MsLUWdkOjtUIYEkfQIXSqyFBHlG+XtMIRBdrjsMPMi53W94wBTJyRSt+m/SC4XMqX4kzU8XN29VVJv5XyFpfWpBdEpjAs3oFYMTv2lkcqe19JKqkn0biHIoUL8BRG6VGwpZmbYTG+HIQwis92Mj9IHpdz7fyI0iQngdOIoKkITHz/g1xNjevpHZYONM2UNLe+nBOEmLQtHByP38gr2I82lrmF1gkh6QCQ9QjfIZXLckhul+O8yIlyou0ChpZAl0Uu8HQpw+Y+1Iy9vUJIeoXeKbQ6yLi7MGRbgQ6XFzvzRwShEkuNV9twclMHBKAxiTCaIpEfohmhDNMWWYhJNok/4WidJEoWWQpbGLPV2KK2UISHI9XrsOTkYlgx8IiZu0V27cjmHSyKvXJgz0M+L0QlXcuTkok4Uf7svEUmP0CWlXIlbcns7DGEQHKs4xozQGd4Oow2ZTIY6MRFHTq63Qxmx3JLE2UYr5Q4X0JIYjsTlHIYjR14uPjNneTuMIUMkPUKXJEmMchgJPJKHBkfDkKyBoomPx547OEmP+N8ODo+HUxYrda6WLztyYKyvjgkG787kE3pGcrmw5xdguv0Ob4cyZIikR+hSvb0ek8bk7TCEAXa0/Cizw7xbk6cj6sRELDt2tFbhFfpXs9vDSUszzW4PEqCSyZho0DFNJW4Rw5mjqAicTjFz6wrif7TQpYrmClEg7BomSRJuyY3VacVX7evtcNqlSUzA09iIq7IKVWjIgF3HI0mMhHG3DS43GZZm7J6WZT91chmTDT74KhXeDk3oR468SzO3xJieS0TSI3RJLhOL1F2rDpYcxOK0UGerY3Xiam+H06HWGVy5OQOa9Ng8EpprcFHGKoeT041WXBf77vwUcmb46dEqrr3XKlxmz8lB7uuLMiTY26EMGSLpEbokxvRcm46UHSHRlEiofuivyaOOjgaVCntOLvqUlAG7jtXtQXcNJD1XTh8HCFIpmWcyoBwJzVhCK0dOLuqEBNElfAWR9AidOlV9itH+o70dhtDPmp3NuCX3sEh4oGXhUXVsDI4BHsxs83jQKobXDeKr08dlQKT2iunjwohlz81FI4oStiGSHqFdRZYidhbuJNYvlglBE7wdjtDPUstTmRs519th9IgmIXHAZ3DZPEO/pccjSZxtslFud7ZuGyWmjwtfIUkSjtxcDNcv83YoQ4pIeoSrOD1OPsr+iPKmcpbHLvd2OEI/c7qdyGSyIbHERE+oE+Ixv//BgF7D5pEIUg2twbxXJjkSl6aPaxnvq/N2aMIQ5qqswtPYKFp6vmJ4/dUTesTtcaOQKyhpLCHfnM+00GnolO3/ofRIntYBy6WNpdyUcBNuj5tw3/DBDFkYBKkVqcwOH5pT0zujHTOGmqoqXDU1KAMDB+QaVrcHnda7LT0eSeJck43Siy05IskResOedQ4AzWgxPOFKIum5Bp2pOUN5U3nrN/kwfRgzw2ZysuokVpcVo8ZIkC6IzOpMwvXh+Gv82Za/jQcmPkC+OZ88cx5LYpaIpvJrlMvjQqPQeDuMHtNNmQKANT0dw9KBWSbD5vGgHeTBvpeSnLKLSY4MSNZrGSeSHKEPmk+cQBEYiCo62tuhDCki6bnGlDWW4XA7WBJz9RpFl1ZKL2ks4WzNWUJ9QjHbzdhcNjyShy/yv2CM/xiWxg6ddZeE/mW2mzGoh+fCg8rwcJShoVhPnBiwpMfhkVANcLLvkSSyrmjJEUmOMBCsJ9LRTZ0ivrx+hUh6riGSJHGq5hTXx17f6X6RvpFE+kYCLbOzShtLuTHhRqIMUYMRpuBFGVUZzImY4+0wekUmk6GbOpXmtBMDfp3+1F6Sk6TXMlYkOcIAkVwurBkZBK//jrdDGXJE0nMNOVNzhqkhU3t0zISgCWJ21ggiIaGQD62Buj3hM3UKlb/5LR6HA7la7e1w2iVJElnNNkpsl5OcMXotS0WSIwwSW1YWUnMzuqk9ux+MBCLpuYaUN5UzPmi8t8MQhphDpYcYZRqFSq7CT+3n7XD6RDd1KpLDgf3MmdYxPv2pN2087SU5o/ValgaKJEfwDuuJdFCp0I4X94OvEknPMJZdl01+Qz4SEoHaQBJNYn0V4TK3x82XhV8yPXQ6OfU5nKs9x91j7/Z2WH2iHTsWmVZL84n0AUl6ulN7XJIkzjfbKbZdLgYokhxhKLGeOIFu3DjkmuE3YWGg9Wlu5ksvvYRMJuN73/te6zabzcb69esJDAzE19eXdevWUVFR0el57r33XmQyWZuflStXttknLi7uqn1eeumlNvtkZGQwf/58tFot0dHR/PKXv+zLyxvSKpsrqbHVsCx2GYuiFhHsE0ycMc7bYQlDhNVlZWv+VhZGLSRIF8Ts8NnckXzHsO7aApCpVOgmTMB6YmDH9VxJkiTON9nYUdPAjpoGdtVa0MllLA30Y2mgH0sC/YjWDs2uNmFksp44Ibq2OtDrlp7U1FRee+01Jk2a1Gb7k08+yZYtW9i0aRNGo5HHHnuMtWvXcuDAgU7Pt3LlSjZu3Nj6WNNOhvr888/z4IMPtj42GC7PQmloaGD58uUsW7aMV199lczMTO6//35MJhMPPfRQb1/mkBWkCyKrNgsAlUJFtEFMSxQuO1R6iFXxq9osFjscp6m3Rzd1KvUffYgkSf0+6FhGS5KT3Wyn8GJLDsBoHw1LA4d316AwMjgrKnCWlqKbJpKe9vQq6WlsbOSuu+7i9ddf58UXX2zdbjabeeONN3jnnXdYsqRlyvTGjRsZO3Yshw8f5rrrruvwnBqNhrCwsE6vazAYOtznX//6Fw6HgzfffBO1Ws348eNJT0/nt7/97TWZ9MiQIXWrMV4Yacx2M35qvzYJz7VEN3UqNa+/jrOkFHVUZL+eW6+Qs6vW0rKsg0hyhGHIeiIdYEC6f68FvfqruH79em688UaWLWu7psfx48dxOp1tticnJxMTE8OhQ4c6Pefu3bsJCQkhKSmJRx99lJqamqv2eemllwgMDGTq1Kn86le/wuVytT536NAhFixYgPqKGR0rVqwgKyuLurq6dq9pt9tpaGho8zMcNDub2Zq/lcnBk70dijAEHa84zvTQ6d4OY8Dopk4BwHoird/PPcvky5JAP2J010armDDyWE+koYqKQhUS4u1QhqQet/S8++67pKWlkZqaetVz5eXlqNVqTCZTm+2hoaGUl5d3eM6VK1eydu1a4uPjycnJ4Uc/+hGrVq3i0KFDKBQtYxC++93vMm3aNAICAjh48CDPPvssZWVl/Pa3v229dnx8/FXXvfScv7//VdfdsGEDzz33XI9e/1BwsPQgK+JWXLPf5IXeq7fV46f2u6YLkin9/VHHx2M9cQLj177m7XAEYUhpPpEuxvN0okdJT1FREU888QTbt29Hq9X2WxB33HFH678nTpzIpEmTSExMZPfu3Sy9WHn1qaeeat1n0qRJqNVqHn74YTZs2NDu+J/uePbZZ9uct6GhgeghXrI7qzaLOL84kfAI7UqrTGNx9GJvhzHgdFOn0nyxGV8QhBYemw3bmTMYb7nZ26EMWT26cx4/fpzKykqmTZuGUqlEqVSyZ88e/vCHP6BUKgkNDcXhcFBfX9/muIqKii7H61wpISGBoKAgsrOzO9xn9uzZuFwu8vPzAQgLC7tqltilxx1dW6PR4Ofn1+ZnKKtqrqLWVsso/1HeDkUYgupsdRg1xmu6lecS3dQp2LOycDc2eTsUQRgybKdOgcuFj2jp6VCPkp6lS5eSmZlJenp668+MGTO46667Wv+tUqnYsWNH6zFZWVkUFhaSkpLS7esUFxdTU1NDeHjHK3ynp6cjl8sJudhvmZKSwt69e3E6na37bN++naSkpHa7toYbh9vB8crjpER0/30Urn3V1mq+yP8Cu9tOWmUa00KmeTukQeEzdSp4PNgyM7wdiiAMGc0nTiD38RErq3eiR0mPwWBgwoQJbX70ej2BgYFMmDABo9HIAw88wFNPPcWuXbs4fvw49913HykpKW1mbiUnJ/Phhx8CLTPBfvCDH3D48GHy8/PZsWMHN998M6NGjWLFihVAyyDl3/3ud5w8eZLc3Fz+9a9/8eSTT3L33Xe3JjTf+MY3UKvVPPDAA5w+fZr33nuP3//+9226r4aTiqYK/nX2X5jtZupsdXxZ8CXLYpZ1faAwYmRWZXK+9jyLYxZzsOQgIbqQEdHKA6BOSEBuNNJ87Li3QxGEIcN6PA3t5EnIlKLucEf6/Z15+eWXkcvlrFu3DrvdzooVK3jllVfa7JOVlYXZbAZAoVCQkZHBW2+9RX19PRERESxfvpwXXnihdayORqPh3Xff5Wc/+xl2u534+HiefPLJNgmN0Wjkiy++YP369UyfPp2goCB+8pOfDMvp6jaXjZNVJ/lG8jc4VnEMgFXxq0bMDU3o2tGyo4Tpw5gYPBGAxTHX/jieK8nkcvTXXUfjvn0EP/6Yt8MRBK/z2O00HTlC0Hce9XYoQ5pMkiRR7OWihoYGjEYjZrPZa+N7JEnis7zPWBm3cthXzxVa5JpzyTfntykO6PQ4mRY6rVdrYWVUZaBX6Uf8siP1H31E2Q+fZfS+vSiDg70djiB4VeOePRQ9/AgJmz9FM2rkjfvs7v1btIENMQdLDzI/ar5IeK4Rh0oPEaANYEnMkjbbPZKH4xXHaXA0MDNsZreTH6vLitluZlLwpK53vsb5LlgAMhmNe/ZguvVWb4cjCF5l2bULVXQ06sSR/WWoK2Le8xDi9DhxS+5hvxK20GJv8V7i/OJICki66jm5TM7MsJksiV7CsfJj3T7n4dLDYjD7RcqAAHRTpmDZvdvboQiCV0mSROPuPfguWiSGQXRBtPQMIallqcwKm+XtMIR+sK94H8kByYT4dF4VVSaTYdKYqLfVY9Ka2t3H4rCQVpGGTCYjyhCFUi5+bS/xXbyY6r/8BY/dLlaUFkYs+9mzuMrLMSxe5O1QhjzR0jNESJKEzW1Dq+y/oo+Cd3yS8wlj/Md0mfBcMjVkKicq2181PL0yncyqTBZELWBB1AJG+4upqFcyLF6EZLXSfOSIt0MRBK+x7NqF3NcXnxkzvB3KkCeSniHiROUJogxR3g5D6AdahbbDVpv2yGQyVAoVDrejzfbChkI0Cg1zIueIJusOqEeNQhUVhWXXLm+HIghe07h7D/p585Bdsfak0D6R9AwR00KnUdhQSLOz2duhCL1U0VTBsfJjxPrFtpmp1R2zwmZdNbYnqy6LsYFj+zPEa45MJsN38WIad+9BTEQVRiJnZSW2zEzRtdVNIukZQpbELOFQaeer0QtDV6GlEJ1K1+7A5a64JTcqhar18YnKE0wJntKP0V27DIsX4Sorw37unLdDEYRB17hnD8jl6Bcs8HYow4JIeoYQuUyOj8oHm8vm7VCEXvBR+RCu73jplM5kVmUyIWgC0DKLr95WT7CPqD3THT4zZiDX60UXlzAiNe7ajW7qVJTXwHJLg0EkPUPMjLAZrVWYheHFR+nT6+5Jm9uGTqkD4GDJQeZGzu3P0K5pMrUa/bx5NO7e4+1QBGFQeWw2mg4eFF1bPSCSniFGJVd1vZMwJOlVeppdPU96mp3NyGUtv4o11hp81b6oFWJAYk/4Ll6ELSMDV1WVt0MRhEHTdPgwks2G7+KRtQxNX4ikRxD6SW9bevYU72FOxBygZSzPSFkpvT/5LlwIcnnL+AZBGCEad+1GFRODOiHB26EMGyLpGYLELJTh6UTlCeKN8T06JrU8lemh05HL5Fyou8Ao0ygxPb0XlP7+LdWZd+32diiCMChaqjDvxrBYVGHuCZH0DEEuj4t6W723wxB6SKPQ9KhbqrypHLlMTohPCJIkUdBQQJwxbuACvMb5Ll5E08GDeJqavB2KIAw426lTuCoqRNdWD4mkZwhqdDaiU+m8HYbQQw6Po3UwclfcHjfpVelMD50OwKGyQ8wOnz2Q4V3z/FbdgGS10vDFdm+HIggDzvzhRyiDg0UV5h4SSc8QZNKYelzcTvAuj+TB4rB0e/89xXtYGLUQgAZHAzJkGNSGgQpvRFBHReKTch3m99/3diiCMKA8djvmzZsx3nILMqVYi68nRNIzBDW7msW4nmGk0dHI53mftyYxXcmqzSLGENPaKnS07CjXhV83kCGOGKa1a2k+dgxHYaG3QxGEAdO4YweehgaMa9d4O5RhRyQ9Q9D8yPnsLd5LZlWmt0MRulDUUERqeSo3xN+Aj8qny/2bnE2UN5Uzyn8UANl12SQYE8RAxH5iuP565AYD9R9+6O1QBGHA1H/wIbqpU9HE92zihCCSniHJR+XDwuiFqBVqsmqzvB2O0I7zdefZXbSbBmcDi2MWd5i0SJKE3W2nzlZHsaWY3UW7mR81H2jpEstvyCfBJKab9he5VovfDTdg/vAjJLfb2+EIQr9zlpXRdOAApnVrvR3KsCSSniEsKSCJ4sZib4chXMHhdvDeuffQKrQsil7E+MDx7e636fwm9pfs50DpATKqMihtKsXpcbIkZklrIcLDpYdFt9YAMK1dg6u8nKZDh70diiD0O/PHHyPTajGsXOXtUIYlMQJqiEswJlDQUECsX6y3QxnxGh2N7C3ey7ox61DKO/7VqWiqYFLQpE4XHm1wNLSsEK72HYhQRzTtpEmoRyVi/uB9fOeJ5TyEa4ckSdR/8CF+y5ej8NV7O5xhSbT0DHE6pU4sQDpEHCk/wqr4VZ0mPAA59TkkmhI7P1fZEdHKM0BkMhmmNWuxfLkDd329t8MRhH5jPXYMZ2EhRtG11Wsi6Rni8hvySTCKMR/eVtFUgZ/ar9sDjh1uR4fPXai7QKIxUQxeHkDGm1cjud2Yt2zxdiiC0G/q3/8AVXQ0PjNnejuUYUskPUPc+MDxHK887u0wRrT9Jfs5V3uOmWHd+0MzO3w2h0oPtfucR/JQ0FAgBi8PMGVQEL4LF2L+QMziEq4N7sYmGrZtw7R2jfjC1Aci6RniDGpDp60GwsDaUbiDUaZRLIzuXg0eAIVcQZg+jJLGkqueO1R6SHRrDRLTurXYTp/GliVmQArDn2XbViSbDeMtt3g7lGFNJD3DwKXZPsLgOlx2mCnBUwjTh/X42PFB4zlTc6bNtiJLEXqVXgxeHiS+CxagCAzE/MEH3g5FEPqs/v0P0KekoAoP93Yow5q4mw4DHsnj7RBGnGJLMT5KHwJ1gb0+x6SgSWRUZQBgd9s5V3uOKSFT+ilCoSsylQrj6tWYP/kUySFaS4Xhy56XhzUtTQxg7gci6Rni3B63WJJikJ2uOU2eOY9JwZP6dJ5QfSg11hrSK9PZX7KfxdFiNeTBZlq7BnddHZadO70diiD0mvn995H7+WFYtszboQx7IukZ4jKrM0XrwCCqaq6i2dncWjW5ryYGTyTCN4KlMUu7nOou9D/N6NH4zJxJzZsbxZcHYVhyWyzUvfsepnXrkGvEQtR9JZKeIc7isGDUGL0dxoiQWp5KRlVGt2dpdUeQLogQn5B+O5/Qc4EPPYQtI4PmI0e8HYog9Fjdu+8i2e0E3Huvt0O5JoikZ4hzS2L9oMGwt3gv8cZ4lsYu9XYoQj/Tz5uLZtxYav76urdDEYQe8djt1L71D4y33IwqVHx56g8i6RniRJfIwJEkCbPdzMGSgyQYEwjSBXk7JGEAyGQygh58kKaDB7GeOu3tcASh28wffoi7pobABx7wdijXjD4lPS+99BIymYzvfe97rdtsNhvr168nMDAQX19f1q1bR0VFRafnuffee5HJZG1+Vq5c2fp8fn4+DzzwAPHx8eh0OhITE/npT3+K44oZGfn5+VedQyaTcfjw8F10sMnZhE6p83YY15xiSzE7CnbwZeGXnK09S7wxnihDlLfDEgaQYflyVLEx1Pz1r94ORRC6RXK5qHnjTQwrVqCOi/N2ONeMXjcjpKam8tprrzFpUtsZLk8++SRbtmxh06ZNGI1GHnvsMdauXcuBAwc6Pd/KlSvZuHFj62PNFQO2zp07h8fj4bXXXmPUqFGcOnWKBx98kKamJn7961+3Oc+XX37J+PGXV74ODOz9lGNvO1l5sl/Hl4x0kiRxpuYMVpdVdGONMDKFgsAHHqD8pz/DnpuHJiHe2yEJQqcatm7DWVRE1O9/5+1Qrim9aulpbGzkrrvu4vXXX8ff3791u9ls5o033uC3v/0tS5YsYfr06WzcuJGDBw922eKi0WgICwtr/bnyvJcSouXLl5OQkMDq1av5/ve/zwftFB0LDAxscx6VStWblzgkuCQXKsXwjX8ocXvcbM7djFFjZEbYDG+HI3iB8ZZbUAYFUfPG37wdiiB0SpIkal5/Hf28eWjHjfN2ONeUXiU969ev58Ybb2TZV2oGHD9+HKfT2WZ7cnIyMTExHDrU/lpEl+zevZuQkBCSkpJ49NFHqamp6XR/s9lMQEDAVdtXr15NSEgI8+bN45NPPun0HHa7nYaGhjY/Q0Wzsxmn2+ntMK4JdbY6thdsZ1nsMtGNNYLJ1WoC7r0X8yef4iwv93Y4gtChpr17sWdlEfjgg94O5ZrT46Tn3XffJS0tjQ0bNlz1XHl5OWq1GpPJ1GZ7aGgo5Z38kVm5ciX/+Mc/2LFjB7/4xS/Ys2cPq1atwu1uf+ZSdnY2f/zjH3n44Ydbt/n6+vKb3/yGTZs2sWXLFubNm8ctt9zSaeKzYcMGjEZj6090dHQXr37wqBVqml3NorZIH1U2V5Jemc7K+JVifJSA6fbbket01G78u7dDEYQOVb/+OrrJk/GZJYY39LcejekpKiriiSeeYPv27Wi12n4L4o477mj998SJE5k0aRKJiYns3r2bpUvbjr0oKSlh5cqV3HbbbTx4RRYcFBTEU0891fp45syZlJaW8qtf/YrVq1e3e91nn322zTENDQ1DJvFRypXMiZhDRnUGk4MnezucYelU9SnMdjOLY0QlZKGFwleP/13foPatfxD4yMMor+hGF4ShoDktDeux40T9+U9iNfUB0KOWnuPHj1NZWcm0adNQKpUolUr27NnDH/7wB5RKJaGhoTgcDurr69scV1FRQVhY9xdtTEhIICgoiOzs7DbbS0tLWbx4MXPmzOGv3ZiFMXv27KvOcSWNRoOfn1+bn6EkUBeIxWHxdhjDhtvj5mjZUZweJ+mV6fiqfJkbOdfbYQlDTMA3vwkeD3X/esfboQjCVWr++jrqUYn4LhZf1gZCj5KepUuXkpmZSXp6euvPjBkzuOuuu1r/rVKp2LFjR+sxWVlZFBYWkpKS0u3rFBcXU1NTQ/gVq8mWlJSwaNGi1sHRcnnXoaenp7c5h3DtsjgsbMvfxmj/0ewq3IWvypc4Y5y3wxKGIGVAAKZbb6Xun//E09Tk7XAEoZUt6zyNu3cT9OCDyLpxjxN6rkfdWwaDgQkTJrTZptfrCQwMbN3+wAMP8NRTTxEQEICfnx+PP/44KSkpXHfdda3HJCcns2HDBtasWUNjYyPPPfcc69atIywsjJycHJ5++mlGjRrFihUrgMsJT2xsLL/+9a+pqqpqPdelFqS33noLtVrN1KlTAfjggw948803+dvfhvdMDTGmp2tmu5kjZUdYFb8KmUzG8rjl3g5JGOIC77uXunffpe4/mwi8715vhyMIANS8/jqqiAj8brjB26Fcs/q93O/LL7+MXC5n3bp12O12VqxYwSuvvNJmn6ysLMxmMwAKhYKMjAzeeust6uvriYiIYPny5bzwwguttXq2b99OdnY22dnZREW1nX1zZVLwwgsvUFBQgFKpJDk5mffee49bb721v1/ioJEkSfTpdsHmsnGo9BAr4laI90roNlVkJMZbbqbm1VcxrbkFxVcmXwjCYLNmnqJh82bCfvYzZMO41MpQJ5NEU0KrhoYGjEYjZrN5SIzvcXlcbM7dzLKYZdjddgJ1w7fQ4kBweVxszd/KqrhVKOQKb4cjDDOuqipyVqzEuG4dYf/vR94ORxjBJEmi4K678VgsxH/4ATKlWH6op7p7/xadhkOYUq7k+tjryajKYGfRTpqdzd4OadC5PW72l+xnX/E+jlccb90uSRJf5H/B9bHXi4RH6BVlcDCBjz5C3TvvYO9kwoMgDLSGzz7DmpZG6I+eFQnPABNJzxCnV+mZEzmHWxJvYV/JPm+HM6iyarPYXbSb6aHTmR81nwh9BJ/nfY7dbedA6QHmRs5Fo9B0eR5B6EjAPfegioig4qVfiPFzgld4rFYqf/VrfJctRd+DCT9C74ikZxg4W3MWp8eJj9LH26EMCrfHzWe5n6FX6Vkau7S1qGC4bzjXx17PtvxtmDQmjBqjlyMVhju5Wk3oM0/TtH8/jXv2eDscYQSqeeNN3DU1hD79tLdDGRFEO9oQJ0kSF+ovUGApIMYQ4+1wBpwkSXxR8AVLY5e224qjlCtZndh+sUlB6A3fpUvxue46Kl/6Bb5z5iBTq70dkjBCOMvKqPnb3wi49x7UMdf+3/ehQCQ9Q9De4r00u5rRK/U4PU6WRC/BV+3r7bAGxd7ivaLbShhUMpmM0GefJW/NGmr/9Y6Ywi4Mmspf/wa5ry+BDz/i7VBGDNG9NYRszdsKtHTvrIxbyfyo+SyJGTkJz6nqUyQYE/BTe3/mnDCyaJPGYLr961S/8gqu2lpvhyOMAM1paTRs2ULIk99D4av3djgjhkh6hpCM6gz2Fe/DRzUyxu5cqdnZTK2tlmi/obH2mTDyBH/3uyCTUfX7P3g7FOEaJ3k8VPz8/9COH49xzRpvhzOiiKRnCHB5XGwv2M5T05+irKmM2eGzvR3SoNtfsp95kfO8HYYwgin9/Ql+bD31mzZhO3fO2+EI1zDzRx9jO326ZYq6WG5iUIl328s8kodt+dtIDkjm/fPvc0P8yCs/frrmNOMCxyGXif+Ognf533kn6rg4Kv5vg5jCLgwId2MTlS//Fr8bVuEzfbq3wxlxxF3Gy/597t/U2+uJNkRze/LtI2b8ziWFDYWYbWaiDFFd7ywIA0ymUhH67A9pPnoUyxfbvR2OcA2qee01PA0WQr7/fW+HMiKJpMeLMqoymBk2k9uTbvd2KF7hkTxk1WUxJ3KOt0MRhFa+8+fju2gRFS++iKuuztvhCNcQa+YpajZuJPDBB1FFRHg7nBFJJD1ecrr6NBqFhjH+Y1DKR2blgIOlB5kTIRIeYegJe+45JIeD8p/+THRzCf3CY7VS+vTTaMeMIeihB70dzoglkh4vqbZWkxSQ5O0wvKbGWoNGoUGvElM1haFHFRpC2HPPYfniC8wffeztcIRrQOWvfo2ztJSIX/1SFMD0IpH0eIHb48bpcXo7jH5Vba1mX/E+thdsx2w3d7pvk7OJI2VHmBE6Y5CiE4Se81u5AuMtt1Dx4os4iou9HY4wjDXu3UvdO+8Q8vQP0CQmejucEU0mibbbVt1dmr6vvsj/goXRC4d11WGz3cyp6lN4JA8eyUOQLoixgWORIWN7wXZmhM0gQBvQ7rE7CnewOHqxmK0lDHnuxkbybr4FZWgosf/8BzKFwtshCcOMq66O3NWr0SaPJfqvryGTybwd0jWpu/dvcdfxAr1KP2wTnmZnM18WfElOfQ6zw2czP2o+C6MXMj5oPHKZHJlMxuKYxZyrbb/OSU59DtGGaJHwCMOCwteXiF+8hPXECWr+9oa3wxGGGUmSKPvxj8HpIvznL4qEZwgQdx6h22pttewv2c+SmCVMC53W4QBslVyFSq4itTz1qucKGgoY4z9moEMVhH7jM2MGgQ8+SNUf/4j11GlvhyMMI+YPPqDxyx2EPf8cqpAQb4cjIJKeQef2uL0dQq9IksTR8qMsj1verVaamWEzifSNZH/J/tZtp6tPM9o0eiDDFIQBEfzYerRjxlD69NN4rFZvhyMMA46iIip+/n8Y167Fb/lyb4cjXCSSnkF2ru7csJy1lVaZxuywni2PEeEbwWjTaL7I/4K9xXuxu+1ibS1hWJKp1UT86pc4S0qo/PVvvB2OMMRJLhelTz+DIjCQ0B/9yNvhCFcYmQVivESSJIotxYwPHO/tUHqs0dGIv9a/x8eF6kNZrhffcoThT5OYSMgPfkDFiy/iu2ghvvPnezskYYiqef11rCdPEvv222IF9SFGtPQMEo/k4fO8z5kVNsvbofRYUUMRofpQb4chCF7nf9c30M+bR+mPfiSqNQvtsmaeourPrxD40IP4TJvq7XCErxBJzyA5XHqYORFzetVa4k1uj5uztWdJDkj2diiC4HUymYzw//s5uNyUPPE9JIfD2yEJQ4izspLiJ76LNjmZ4PXrvR2O0A6R9AwSDx5MWpO3w+ixA6UHmBc5z9thCMKQoQoJIepPf8R64gRlP/mpWKZCAMDT3Ezxd9aDy03Un/6ITKXydkhCO0TSM0iG4x/G0sZSjBojPiofb4ciCEOKz/TphP/fzzF/9BE1r73m7XAEL5M8HkqfeQZ7bi5Rf3kFVViYt0MSOiAGMg+S4VSUyu1xc6D0AAqZgrmRc70djiAMScavfQ1HQSFVv/s96pgY/G64wdshCV5S+evfYNmxk6g//Qnd+OE3UWUkEUnPIJExfJKe3UW7mRs5F61S6+1QBGFIC1r/HRyFBZT+8FmUYeFi4OoIVPfue9S++SahP3oWw5LF3g5H6ILo3hLayK3PJd4YLxIeQegGmUxG+Isvops0ieL163EUFno7JGEQNe7bT/kLL+D/jW/g/81vejscoRtE0jNIJIbHmJ68hjwSTAneDkMQhg25Wk3kH/+Aws+PoocfwW02ezskYRDYzp+n5HvfQz93DqE/enZYDWEYyUTSM0hCfUIpaSzxdhidyqrNIsl/+FWLFgRvU/r7E/3aq7hrayn+7hNiKvs1zlVVRdEjj6CKiiLyty8jU4qRIsOFSHoGySjTKHLqc7wdRody6nMoaCggyhDl7VAEYVhSx8UR9ac/0pyWRtnPnhuWMzaFrnmsVoq+sx6cLqJfe1VUXB5mRHo6SIZa06fb46bQUkhpYyk2l414UzzL48RyEYLQFz4zZxLx8xcpffoZVFGRBH/nO94OSehHLWtqPY09O5vYf/5TTE0fhvrU0vPSSy8hk8n43ve+17rNZrOxfv16AgMD8fX1Zd26dVRUVHR6nnvvvReZTNbmZ+XKlW32qa2t5a677sLPzw+TycQDDzxAY2Njm30yMjKYP38+Wq2W6OhofvnLX/bl5fW7QG0glc2VA34dp8fJwZKD7C/Zz77ifewp2oPD3dLcnlufy87CnRwqO4RcJiclIoWlsUtJMIpxPILQH4yrVxP03cep/sMfqfrTn0WLzzXC43BQ8uSTWHbtJvI3v0Y3QUxNH4563dKTmprKa6+9xqRJk9psf/LJJ9myZQubNm3CaDTy2GOPsXbtWg4cONDp+VauXMnGjRtbH2s0mjbP33XXXZSVlbF9+3acTif33XcfDz30EO+88w4ADQ0NLF++nGXLlvHqq6+SmZnJ/fffj8lk4qGHHurty+xX4wLHsa9kHyE+IQNy/mPlxzA7zOiUOqaHTUejaHkPnR4nR8uO4nA7CPEJYUnMkgG5viAILYK/8x1kShVVv/0tHouFkB8+M+Rae4Xu8zQ3U/z4d2lOTSXqT3/EsGiRt0MSekvqBYvFIo0ePVravn27tHDhQumJJ56QJEmS6uvrJZVKJW3atKl137Nnz0qAdOjQoQ7Pd88990g333xzh8+fOXNGAqTU1NTWbZ9//rkkk8mkkpISSZIk6ZVXXpH8/f0lu93eus8zzzwjJSUldft1mc1mCZDMZnO3j+mpvUV7+/V8bo9bcrqd0vna81JefV6/nlsQhL6p+de/pDNJyVLp//6v5HG5vB2O0AuuhgYp785vSOemTpMaDx32djhCB7p7/+5V99b69eu58cYbWbZsWZvtx48fx+l0ttmenJxMTEwMhw4d6vScu3fvJiQkhKSkJB599FFqampanzt06BAmk4kZM2a0blu2bBlyuZwjR4607rNgwQLUanXrPitWrCArK4u6DlZDttvtNDQ0tPkZaEq5EpvL1i/nOlNzhj1FezhafhSz3UycMa5fzisIQv8I+MY3CH9pA/Xvf0DpD34gZnUNM67aWgrvuRd7Tg4xG99Ef91sb4ck9FGPu7feffdd0tLSSE1Nveq58vJy1Go1JpOpzfbQ0FDKy8s7POfKlStZu3Yt8fHx5OTk8KMf/YhVq1Zx6NAhFAoF5eXlhIS07RJSKpUEBAS0nre8vJz4+PirrnvpOX//q1c337BhA88991y3Xnd/mR46nX0l+1gas7RP59lRsINw33AWx4gKoIIwlJluuQW5jw8l//N9PE3fJfL3v0OuFcU/hzpnRQWF992Pu6GB2H+8hTZJlPO4FvSopaeoqIgnnniCf/3rX2j78Zf2jjvuYPXq1UycOJFbbrmFzZs3k5qayu7du/vtGu159tlnMZvNrT9FRUUDej0AtULNuIBxfFnwJR7J0+PjJUkiz5xHuG844wLHDUCEgiD0N7/ly4n+y19oOnKEoocext3Y5O2QhE44iooouOtuPDYrcW//UyQ815AeJT3Hjx+nsrKSadOmoVQqUSqV7Nmzhz/84Q8olUpCQ0NxOBzU19e3Oa6iooKwHkztS0hIICgoiOzsbADCwsKorGw768nlclFbW9t63rCwsKtmiV163NG1NRoNfn5+bX4GQ7hvOHMj57K/ZH+Pj91fsp+q5ipGm0YPQGSCIAwU33lziXnjb9jOnKHw/vtxf+XvpDA02LOzKfjGXcgUCuLefht1XJy3QxL6UY+SnqVLl5KZmUl6enrrz4wZM7jrrrta/61SqdixY0frMVlZWRQWFpKSktLt6xQXF1NTU0N4eDgAKSkp1NfXc/z48dZ9du7cicfjYfbs2a377N27F6fT2brP9u3bSUpKardry9t0Sh0yZK1TybujvKkcP40fs8JnoVKoBjA6QRAGgs/06cS89XecRUUUfPNbuKqqvB2ScAXrqdMU3P1NFAEBxP7rbVQREd4OSehnMknqWxGJRYsWMWXKFH73u98B8Oijj/LZZ5/x97//HT8/Px5//HEADh482HpMcnIyGzZsYM2aNTQ2NvLcc8+xbt06wsLCyMnJ4emnn8ZisZCZmdk6dX3VqlVUVFTw6quvtk5ZnzFjRuuUdbPZTFJSEsuXL+eZZ57h1KlT3H///bz88svdnrLe0NCA0WjEbDYPSquPw+3gUOkhFkYvbLO9srmSc7XnkCFrXbPL7XGjkCtYELVgwOMSBGFg2XNyKLzvfmRKJZEv/xbd5MneDmnEM2/ZQvmPf4J69Chi/vpXFEajt0MSeqDb9+++ThO7csq6JEmS1WqVvvOd70j+/v6Sj4+PtGbNGqmsrKzNMYC0ceNGSZIkqbm5WVq+fLkUHBwsqVQqKTY2VnrwwQel8vLyNsfU1NRId955p+Tr6yv5+flJ9913n2SxWNrsc/LkSWnevHmSRqORIiMjpZdeeqlHr2Uwpqx/1amqU9KF2gutj6ubq6VdhbsG7fqCIHiHo7hYyv3616Uz4ydI1W9ulDwej7dDGpHcVqtU+uOfSGeSkqXip/5Hcjc2ejskoRe6e//uc0vPtWSwW3ouOVx2GKfbiUwmw+aysSx2WdcHCYIw7ElOJ5Uv/47aN9/Ed9Eiwjf8H8oh2B1/rbLn5lLyvSdxFBQQ+r//D9Ott4oiksNUd+/fIum5greSHkEQRjbL7t2U/fBZZDodkb/5NT7Tpnk7pGte/UcfUf7c86jCw4l8+WW0SWO8HZLQB929f4tV1gVBELzMsGgR8R99iCo8nIJvfovqv76O5Ol5SQuha57mZkqf/RFlP3wWvxUriP/vJpHwjCAi6REEQRgCVGFhxP7jLQIfeICq3/6WoocfwVVb6+2wrin2CxfI+/rXadi6lfANG4h4aQNyHx9vhyUMIpH0CIIgDBEypZKQp54k+vXXsZ0+Td4ta2g6etTbYQ17kiRR/9//knfb15HJ5MT/dxOmNbd4OyzBC0TSIwiCMMT4zp9H/Icfoo6Lo/De+yj72c9wXbEeodB99pwcih55hLL//THGr91E3H/eQ5OY6O2wBC8RA5mvIAYyC4IwlEhuN7X//CfVr/wFPB6CHnkY/29+E/nF+mVCx1x1dVT/8U/UvfceqvBwQp55Gr/rr/d2WMIAEbO3ekEkPYIgDEWuujqq//wKdf/+d8sN/Pv/g2HFCjG9uh0eh4O6t/9F9V/+ApJE0KOP4v/Nu5Gr1d4OTRhAIunpBZH0CIIwlNlzc6n81a9p3LUL3dSphD77Q3STJnk7rCFBkiQs27dT+evf4Cwpwf/2rxP02GMoAwK8HZowCMSUdUEQhGuMJiGB6L+8Qsybb+Bpbib/67dT8oOncZaWejs0r7JmnqLgm9+k5LtPoI6PI+Hjjwj7yU9EwiNcRbT0XEG09AiCMFxIbjfmDz+k8ne/x2OxEHDPPfjffReqkBBvhzZo7NnZ1Lz+N8wff4xm9ChCnvkhvvPmejsswQtE91YviKRHEIThxt3YRM3fXqf2728hOZ34LlqE6bZb8Z0/H5lC4e3w+p3HaqXh863Ub9qE9cQJFEFBBD/+OKZ1a5Epld4OT/ASkfT0gkh6BEEYrtwNDZg3b6Z+03+xnz2LMiwM09q1mNatRRUZ6e3w+sx25gx1mzbR8OlmPI2N6OfMwfT12zAsWYJMDFIe8UTS0wsi6REEYbiTJAnbqdPUb9pEw+bNeKxW9PPmYbrtVgyLFyNTqbwdYre5Gxtp2LyF+k2bsJ0+jTIkBOPaNZhuvRV1VJS3wxOGEJH09IJIegRBuJZ4mppo2LqV+v9swnryJIqgIEy33Ix+/gJ0kyYi1+m8HeJV3PX1NJ84geXLL2n47HMkux3fhQtbuuwWLBBdWEK7RNLTCyLpEQThWmXLOk/9f/9Lwyef4DabQalEO24cPtOmoZs+DZ9p01AGBg5qTJIk4Swupvn4caxpJ2hOO44jOwcAVVQUpnVrMa5diyo0dFDjEoYfkfT0gkh6BEG41kkeD/YL2VhPpNF8PA3r8eOtU97VsbHopk/HZ/o0dFOnoY6P69cCiJLLhS0rC+vxNJrTWq7tqqoCQDN6FLppF689bRqqyEhRfFHoNpH09IJIegRBGImcZWUtSUjaCZrT0rCfOweSBAoFCpMJhb8Jpckfhb//xccX/+1vQunvj9zHB7fFgruuDnddHa66Otx19bjr61u3uevqcDc0gCQhU6nQTpp0uZVpyhQUJpO33wZhGBNJTy+IpEcQBAHcFgvW9JM4S4pbkpX6+suJzBVJjKe5+apj5X5+bZMk/yuSJZMRzahRaMePF+uHCf2qu/dvMSJMEARBaENhMOA7f16X+3nsdtz19XiamlD4+aEwGofV7DBh5BFJjyAIgtArco0GuRhkLAwjYu0tQRAEQRBGBJH0CIIgCIIwIoikRxAEQRCEEUEkPYIgCIIgjAgi6REEQRAEYUQQSY8gCIIgCCOCSHoEQRAEQRgRRNIjCIIgCMKIIJIeQRAEQRBGBJH0CIIgCIIwIoikRxAEQRCEEUEkPYIgCIIgjAgi6REEQRAEYUQQq6xfQZIkABoaGrwciSAIgiAI3XXpvn3pPt4RkfRcwWKxABAdHe3lSARBEARB6CmLxYLRaOzweZnUVVo0gng8HkpLSzEYDMhkMm+HM2w1NDQQHR1NUVERfn5+3g5H6IT4rIYH8TkNH+Kz8g5JkrBYLERERCCXdzxyR7T0XEEulxMVFeXtMK4Zfn5+4pd+mBCf1fAgPqfhQ3xWg6+zFp5LxEBmQRAEQRBGBJH0CIIgCIIwIoikR+h3Go2Gn/70p2g0Gm+HInRBfFbDg/ichg/xWQ1tYiCzIAiCIAgjgmjpEQRBEARhRBBJjyAIgiAII4JIegRBEARBGBFE0iMIgiAIwoggkh6hR3bv3o1MJmv3JzU19ar9s7OzMRgMmEymTs978uRJ7rzzTqKjo9HpdIwdO5bf//73A/QqRoaB+qwAvvvd7zJ9+nQ0Gg1Tpkzp/+BHmIH8rAoLC7nxxhvx8fEhJCSEH/zgB7hcrgF4Fde+7nxOWVlZLF68mNDQULRaLQkJCfzv//4vTqez03Pv2LGDOXPmYDAYCAsL45lnnhGf0wAQFZmFHpkzZw5lZWVttv34xz9mx44dzJgxo812p9PJnXfeyfz58zl48GCn5z1+/DghISG8/fbbREdHc/DgQR566CEUCgWPPfZYv7+OkWCgPqtL7r//fo4cOUJGRka/xTxSDdRn5Xa7ufHGGwkLC+PgwYOUlZXxrW99C5VKxf/93//1++u41nXnc1KpVHzrW99i2rRpmEwmTp48yYMPPojH4+nwPT958iQ33HAD/+///T/+8Y9/UFJSwiOPPILb7ebXv/71gL+uEUUShD5wOBxScHCw9Pzzz1/13NNPPy3dfffd0saNGyWj0djjc3/nO9+RFi9e3A9RCpI0MJ/VT3/6U2ny5Mn9F6QgSVL/fVafffaZJJfLpfLy8tZtf/nLXyQ/Pz/Jbrf3d9gjTmef05WefPJJad68eR0+/+yzz0ozZsxos+2TTz6RtFqt1NDQ0C+xCi1E95bQJ5988gk1NTXcd999bbbv3LmTTZs28ec//7nX5zabzQQEBPQ1ROGigfyshP7VX5/VoUOHmDhxIqGhoa3bVqxYQUNDA6dPn+7XmEeijj6nK2VnZ7N161YWLlzY4T52ux2tVttmm06nw2azcfz48X6LVxBjeoQ+euONN1ixYkWbhVpramq49957+fvf/97rBfcOHjzIe++9x0MPPdRfoY54A/VZCf2vvz6r8vLyNgkP0Pq4vLy8/wIeodr7nC6ZM2cOWq2W0aNHM3/+fJ5//vkOz7NixQoOHjzIv//9b9xuNyUlJa37f7U7TegbkfQIAPzwhz/scIDepZ9z5861Oaa4uJht27bxwAMPtNn+4IMP8o1vfIMFCxb0KpZTp05x880389Of/pTly5f3+jVdq4bSZyV0TnxWw0N/fk6XvPfee6SlpfHOO++wZcuWTsfmLF++nF/96lc88sgjaDQaxowZww033ACAXC5u0/1JLEMhAFBVVUVNTU2n+yQkJKBWq1sfv/DCC/zxj3+kpKQElUrVut1kMtHY2Nj6WJIkPB4PCoWCv/71r9x///0dXuPMmTMsXryYb3/72/z85z/vwyu6dg2VzwrgZz/7GR999BHp6em9ezHXOG9/Vj/5yU/45JNP2nw+eXl5JCQkkJaWxtSpU/vw6q4d/fk5teftt9/moYcewmKxoFAoOtxPkiTKysrw9/cnPz+fcePGcfToUWbOnNmzFyR0SMzeEgAIDg4mODi42/tLksTGjRtbZ4Jc6dChQ7jd7tbHH3/8Mb/4xS84ePAgkZGRHZ7z9OnTLFmyhHvuuUckPJ0YCp+V0D3e/qxSUlL4+c9/TmVlJSEhIQBs374dPz8/xo0b14tXdG3qz8+pPR6PB6fT2ZqkdkQmkxEREQHAv//9b6Kjo5k2bVq34xK6wVsjqIXh7csvv5QA6ezZs13u294skw8++EBKSkpqfZyZmSkFBwdLd999t1RWVtb6U1lZ2d+hjzj9/VlJkiRduHBBOnHihPTwww9LY8aMkU6cOCGdOHFCzAjqo/7+rFwulzRhwgRp+fLlUnp6urR161YpODhYevbZZ/s79BGls8/p7bfflt577z3pzJkzUk5OjvTee+9JERER0l133dW6T3u/U7/85S+ljIwM6dSpU9Lzzz8vqVQq6cMPPxzolzLiiJYeoVfeeOMN5syZQ3Jycq+ON5vNZGVltT7+73//S1VVFW+//TZvv/126/bY2Fjy8/P7Gu6I1t+fFcC3v/1t9uzZ0/r4UjdJXl4ecXFxvY51pOvvz0qhULB582YeffRRUlJS0Ov13HPPPZ0OqhW61tnnpFQq+cUvfsH58+eRJInY2Fgee+wxnnzyydZ92vud+vzzz/n5z3+O3W5n8uTJfPzxx6xatWrAX8tII8b0CIIgCIIwIohh4YIgCIIgjAgi6REEQRAEYUQQSY8gCIIgCCOCSHoEQRAEQRgRRNIjCIIgCMKIIJIeQRAEQRBGBJH0CIIgCIIwIoikRxAEQRCEEUEkPYIgCIIgjAgi6REEQRAEYUQQSY8gCIIgCCOCSHoEQRAEQRgR/j+n11d3NF173AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in enclaveOnly:\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    ePop = np.sum([unitPop[u] for u in enclaveList])\n",
    "    print(\"for t\",t,\"in-HD and enclave pops are\", HDvPop[t],ePop)\n",
    "    for u in enclaveList:\n",
    "        plotPoly(unitGeom[u])\n",
    "    for uu in HDunitList[t]:\n",
    "        plotPoly(unitGeom[uu],0.2)\n",
    "    #for u in borderSet:\n",
    "    #    plotPoly(unitGeom[u].centroid.buffer(0.004),0.5)\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "12a1b87f-a12d-45c3-9084-6b15e73d513f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "figuring out which 7192-bordering unit we can drop to free\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"figuring out which 7192-bordering unit we can drop to free\")\n",
    "plotPoly(unitGeom[7192])\n",
    "for u in unitNbrs[7192]:\n",
    "    plotPoly(unitGeom[u],0.2)\n",
    "    plotCenter(u,unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "fca5a151-8091-431d-905b-b5ffd72330a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 815494 district pop vs 776661 target\n",
      "working on enclave-y HD 12050 . We have evaluated 0 of 5923 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 12068 . We have evaluated 1 of 5923 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 13002 . We have evaluated 2 of 5923 enclavy HDs.Time is now 0\n",
      "Out of 5923 HDs with enclaves 3 had enclaves.\n",
      "Of these, 3 wouldn't be over 815494 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in enclaveOnly:\n",
    "    HDvPop[t] = np.sum([unitPop[u] for u in HDunitList[t]])\n",
    "\n",
    "#OPTIONAL if still enclaves -- rerun the CANFILL CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):  #internals + edgers):\n",
    "    if iii%1 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "fcb6dde4-9e11-43ce-bd1b-7b54980f7bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final ??? check -- any more disco's ?\n",
      "working on HD 1444 time is now 0 sec\n",
      "working on HD 2999 time is now 8 sec\n",
      "working on HD 4146 time is now 16 sec\n",
      "working on HD 5924 time is now 24 sec\n",
      "working on HD 6482 time is now 32 sec\n",
      "working on HD 7033 time is now 38 sec\n",
      "working on HD 7625 time is now 47 sec\n",
      "working on HD 8140 time is now 54 sec\n",
      "working on HD 10265 time is now 62 sec\n",
      "working on HD 10785 time is now 70 sec\n",
      "working on HD 11301 time is now 78 sec\n",
      "working on HD 11813 time is now 85 sec\n",
      "working on HD 12320 time is now 93 sec\n",
      "working on HD 12837 time is now 100 sec\n",
      "working on HD 13604 time is now 109 sec\n",
      "out of 7263 total HDs, there were 7263 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 114 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "#check contig one more time after enclave-filling these 3 HDs\n",
    "print(\"final ??? check -- any more disco's ?\")  #run again if we ran above block to fill minor enclaves\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "760a9846-3060-4fab-8eb0-62d4de92bf03",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "0.93112     7192     495747 0.25\n",
      "0.99602     7193     52630 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )\n",
    "# note for MN, option 3 here led to tight CCB unit usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7b9b68ef-6ae9-4d80-895d-41a86f48a55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(\"hi\")  #see MN for code to boost any under-used CCBs via border linking strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.99987 0.07346\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 1444\n",
      "working on avg unit-to-HDcp dist for HD 3788\n",
      "working on avg unit-to-HDcp dist for HD 6027\n",
      "working on avg unit-to-HDcp dist for HD 6911\n",
      "working on avg unit-to-HDcp dist for HD 7832\n",
      "working on avg unit-to-HDcp dist for HD 10265\n",
      "working on avg unit-to-HDcp dist for HD 11093\n",
      "working on avg unit-to-HDcp dist for HD 11913\n",
      "working on avg unit-to-HDcp dist for HD 12734\n",
      "working on avg unit-to-HDcp dist for HD 13819\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bhg0bpp07d7arYAAA0L1YfuhZcXGxHA6HCgoKlJSUpLy8PKWmpurYsWMaMGBAs/6NjY2aPHmyBgwYoHfeeUexsbH6/PPPddNNN3VE/QAAIMBZDiNr1qzR008/rczMTElSQUGBSkpKtGHDBmVlZTXrv2HDBp0/f14HDhzQddddJ0mKi4u7tqoB9DhtebLsqZVpfqgEQEezdJqmsbFRFRUVSklJ+f4DgoOVkpKi8vLyFsf85S9/UXJysubOnauoqCiNGjVKK1asUFNTU6v7cblcqq+v99kAAED3ZCmMnDt3Tk1NTYqKivJpj4qKktPpbHHMiRMn9M4776ipqUk7d+7UkiVLtHr1av3qV79qdT+5ubmKiIjwbna73UqZAAAggHT63TRut1sDBgzQW2+9pXHjxmnatGlatGiRCgoKWh2TnZ2turo673b69OnOLhMAABhi6ZqRyMhIhYSEqKamxqe9pqZG0dHRLY4ZOHCgrrvuOoWEhHjbRowYIafTqcbGRoWGhjYbY7PZZLPZrJQGAAAClKWVkdDQUI0bN06lpaXeNrfbrdLSUiUnJ7c4ZsKECTp+/Ljcbre37dNPP9XAgQNbDCIAAKBnsXyaxuFwaP369frjH/+oI0eO6Nlnn1VDQ4P37ppZs2YpOzvb2//ZZ5/V+fPnNX/+fH366acqKSnRihUrNHfu3I47CgAAELAs39o7bdo01dbWaunSpXI6nRo7dqx27drlvai1qqpKwcHfZxy73a7du3drwYIFGjNmjGJjYzV//ny9+OKLHXcUAAAgYFkOI5I0b948zZs3r8XflZWVNWtLTk7Whx9+2J5dAQCAbo530wAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAo3qZLgAAOkpcVslV+5xameaHSgBYwcoIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwql1hJD8/X3FxcQoLC1NSUpIOHjzYpnFbtmxRUFCQpk6d2p7dAgCAbshyGCkuLpbD4VBOTo4OHz6s+Ph4paam6uzZs1ccd+rUKb3wwgu6//77210sAADofiyHkTVr1ujpp59WZmamRo4cqYKCAl1//fXasGFDq2Oampr0xBNPaPny5RoyZMg1FQwAALoXS2GksbFRFRUVSklJ+f4DgoOVkpKi8vLyVse9/PLLGjBggH7+85+3aT8ul0v19fU+GwAA6J4shZFz586pqalJUVFRPu1RUVFyOp0tjtm/f78KCwu1fv36Nu8nNzdXERER3s1ut1spEwAABJBOvZvmwoULmjlzptavX6/IyMg2j8vOzlZdXZ13O336dCdWCQAATOplpXNkZKRCQkJUU1Pj015TU6Po6Ohm/T/77DOdOnVK6enp3ja32/3tjnv10rFjxzR06NBm42w2m2w2m5XSAABAgLK0MhIaGqpx48aptLTU2+Z2u1VaWqrk5ORm/YcPH66PP/5YlZWV3u0nP/mJJk2apMrKSk6/AAAAaysjkuRwOJSRkaHx48crMTFReXl5amhoUGZmpiRp1qxZio2NVW5ursLCwjRq1Cif8TfddJMkNWsHAAA9k+UwMm3aNNXW1mrp0qVyOp0aO3asdu3a5b2otaqqSsHBPNgVAAC0jeUwIknz5s3TvHnzWvxdWVnZFcdu3LixPbsEAADdVLvCCAAEqriskqv2ObUyzQ+VAPgO51MAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGMVzRgDg/+BZJIB/sTICAACMIowAAACjCCMAAMAowggAADCKMAIAAIzibhoggLTlLg8ACDSsjAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjepkuAOgJ4rJKTJcAAF0WKyMAAMAowggAADCK0zQA0A5tOfV2amWaHyoBAh8rIwAAwCjCCAAAMIowAgAAjGpXGMnPz1dcXJzCwsKUlJSkgwcPttp3/fr1uv/++3XzzTfr5ptvVkpKyhX7AwCAnsVyGCkuLpbD4VBOTo4OHz6s+Ph4paam6uzZsy32Lysr0/Tp0/XBBx+ovLxcdrtdDz74oKqrq6+5eAAAEPiCPB6Px8qApKQk3X333Vq7dq0kye12y26367nnnlNWVtZVxzc1Nenmm2/W2rVrNWvWrDbts76+XhEREaqrq1N4eLiVcoEugYee9UzcTYOerq3f35ZWRhobG1VRUaGUlJTvPyA4WCkpKSovL2/TZ3z11Ve6dOmS+vbt22ofl8ul+vp6nw0AAHRPlsLIuXPn1NTUpKioKJ/2qKgoOZ3ONn3Giy++qJiYGJ9A83/l5uYqIiLCu9ntditlAgCAAOLXu2lWrlypLVu26N1331VYWFir/bKzs1VXV+fdTp8+7ccqAQCAP1l6AmtkZKRCQkJUU1Pj015TU6Po6Ogrjl21apVWrlypv/71rxozZswV+9psNtlsNiulAQCAAGVpZSQ0NFTjxo1TaWmpt83tdqu0tFTJycmtjvv1r3+tV155Rbt27dL48ePbXy0AAOh2LL+bxuFwKCMjQ+PHj1diYqLy8vLU0NCgzMxMSdKsWbMUGxur3NxcSdJrr72mpUuXqqioSHFxcd5rS/r06aM+ffp04KEAQNfC+2uAtrEcRqZNm6ba2lotXbpUTqdTY8eO1a5du7wXtVZVVSk4+PsFl9/97ndqbGzUo48+6vM5OTk5WrZs2bVVDwAAAp7l54yYwHNGEOh4zghaw8oIurO2fn9bXhkBAHQcTuUAvCgPAAAYRhgBAABGEUYAAIBRhBEAAGAUF7AC14g7ZQDg2hBGgCsgaABA5+M0DQAAMIowAgAAjCKMAAAAowgjAADAKC5gBYAujkfGo7tjZQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUTxnBN0Sz2UAgMBBGAGAboAAjkDGaRoAAGAUYQQAABjFaRoElLYsRQMAAgthBAB6iI4K81x7go5GGEGPxSoLAHQNXDMCAACMIowAAACjCCMAAMAowggAADCKC1jhFzwdEgDQGlZGAACAUYQRAABgFKdpAACW8PA0dDRWRgAAgFGsjOCa8SRTAO3Bhe34DmGkm+qoP3KCBgCgs3GaBgAAGMXKSBfTk5ctWYUBgJ6JMAIA6LK4c6dnIIwEIFYQAADdCdeMAAAAo9q1MpKfn6/f/OY3cjqdio+P15tvvqnExMRW+2/btk1LlizRqVOndPvtt+u1117TQw891O6i0TFYYQGA7/Xka/ZMsxxGiouL5XA4VFBQoKSkJOXl5Sk1NVXHjh3TgAEDmvU/cOCApk+frtzcXP34xz9WUVGRpk6dqsOHD2vUqFEdchCBgi9/ADCD/3+7tiCPx+OxMiApKUl333231q5dK0lyu92y2+167rnnlJWV1az/tGnT1NDQoPfff9/bds8992js2LEqKCho0z7r6+sVERGhuro6hYeHWym3Q/DMDgBAW3XU6kl3WKlp6/e3pZWRxsZGVVRUKDs729sWHByslJQUlZeXtzimvLxcDofDpy01NVU7duxodT8ul0sul8v7c11dnaRvD8oEt+urq/ZpS21t+RwAQGBry/fBqJzdXWpfnyxP7Yhymvmuvqute1gKI+fOnVNTU5OioqJ82qOionT06NEWxzidzhb7O53OVveTm5ur5cuXN2u32+1WyvWriDzTFQAAugJ/fh901L46u+YLFy4oIiKi1d93yVt7s7OzfVZT3G63zp8/r379+ikoKMhvddTX18tut+v06dNGTg91RcxJc8yJL+ajOebEF/PRXHedE4/HowsXLigmJuaK/SyFkcjISIWEhKimpsanvaamRtHR0S2OiY6OttRfkmw2m2w2m0/bTTfdZKXUDhUeHt6t/nF0BOakOebEF/PRHHPii/lorjvOyZVWRL5j6TkjoaGhGjdunEpLS71tbrdbpaWlSk5ObnFMcnKyT39J2rt3b6v9AQBAz2L5NI3D4VBGRobGjx+vxMRE5eXlqaGhQZmZmZKkWbNmKTY2Vrm5uZKk+fPna+LEiVq9erXS0tK0ZcsWHTp0SG+99VbHHgkAAAhIlsPItGnTVFtbq6VLl8rpdGrs2LHatWuX9yLVqqoqBQd/v+By7733qqioSIsXL9ZLL72k22+/XTt27AiIZ4zYbDbl5OQ0O2XUkzEnzTEnvpiP5pgTX8xHcz19Tiw/ZwQAAKAj8W4aAABgFGEEAAAYRRgBAABGEUYAAIBRPT6M5OfnKy4uTmFhYUpKStLBgwdb7fvAAw8oKCio2ZaW1rVfVGSVlTmRpLy8PN1xxx3q3bu37Ha7FixYoG+++cZP1XY+K/Nx6dIlvfzyyxo6dKjCwsIUHx+vXbt2+bHazrdv3z6lp6crJiZGQUFBV3zP1HfKysp01113yWaz6bbbbtPGjRs7vU5/sTofZ86c0YwZMzRs2DAFBwfr+eef90ud/mR1TrZv367Jkyerf//+Cg8PV3Jysnbv7ph3t3QFVudj//79mjBhgvr166fevXtr+PDhev311/1TrCE9OowUFxfL4XAoJydHhw8fVnx8vFJTU3X27NkW+2/fvl1nzpzxbp988olCQkL02GOP+bnyzmN1ToqKipSVlaWcnBwdOXJEhYWFKi4u1ksvveTnyjuH1flYvHixfv/73+vNN9/Uv//9b82ZM0cPP/ywPvroIz9X3nkaGhoUHx+v/Pz8NvU/efKk0tLSNGnSJFVWVur555/X7Nmzu82XjdX5cLlc6t+/vxYvXqz4+PhOrs4Mq3Oyb98+TZ48WTt37lRFRYUmTZqk9PT0bvN3Y3U+brjhBs2bN0/79u3TkSNHtHjxYi1evLh7P5/L04MlJiZ65s6d6/25qanJExMT48nNzW3T+Ndff91z4403ei5evNhZJfqd1TmZO3eu54c//KFPm8Ph8EyYMKFT6/QXq/MxcOBAz9q1a33aHnnkEc8TTzzRqXWaIsnz7rvvXrHPwoULPXfeeadP27Rp0zypqamdWJkZbZmP/9/EiRM98+fP77R6ugKrc/KdkSNHepYvX97xBRnW3vl4+OGHPU8++WTHF9RF9NiVkcbGRlVUVCglJcXbFhwcrJSUFJWXl7fpMwoLC/Wzn/1MN9xwQ2eV6VftmZN7771XFRUV3lMXJ06c0M6dO/XQQw/5pebO1J75cLlcCgsL82nr3bu39u/f36m1dmXl5eU+cyhJqampbf47Q8/jdrt14cIF9e3b13QpXcJHH32kAwcOaOLEiaZL6TRd8q29/nDu3Dk1NTV5nxz7naioKB09evSq4w8ePKhPPvlEhYWFnVWi37VnTmbMmKFz587pvvvuk8fj0eXLlzVnzpxucZqmPfORmpqqNWvW6Ac/+IGGDh2q0tJSbd++XU1NTf4ouUtyOp0tzmF9fb2+/vpr9e7d21Bl6KpWrVqlixcv6vHHHzddilG33HKLamtrdfnyZS1btkyzZ882XVKn6bErI9eqsLBQo0ePVmJioulSjCorK9OKFSu0bt06HT58WNu3b1dJSYleeeUV06UZ8cYbb+j222/X8OHDFRoaqnnz5ikzM9PnFQkAWldUVKTly5dr69atGjBggOlyjPr73/+uQ4cOqaCgQHl5edq8ebPpkjpNj10ZiYyMVEhIiGpqanzaa2pqFB0dfcWxDQ0N2rJli15++eXOLNHv2jMnS5Ys0cyZM72JffTo0WpoaNAzzzyjRYsWBfSXcHvmo3///tqxY4e++eYb/fe//1VMTIyysrI0ZMgQf5TcJUVHR7c4h+Hh4ayKwMeWLVs0e/Zsbdu2rdmpvZ5o8ODBkr79f7WmpkbLli3T9OnTDVfVOQL3m+IahYaGaty4cSotLfW2ud1ulZaWKjk5+Ypjt23bJpfLpSeffLKzy/Sr9szJV1991SxwhISESJI8Af7ao2v5NxIWFqbY2FhdvnxZf/rTn/TTn/60s8vtspKTk33mUJL27t171TlEz7J582ZlZmZq8+bN3e5xCR3B7XbL5XKZLqPT9NiVEUlyOBzKyMjQ+PHjlZiYqLy8PDU0NCgzM1OSNGvWLMXGxio3N9dnXGFhoaZOnap+/fqZKLtTWZ2T9PR0rVmzRgkJCUpKStLx48e1ZMkSpaene0NJILM6H//85z9VXV2tsWPHqrq6WsuWLZPb7dbChQtNHkaHunjxoo4fP+79+eTJk6qsrFTfvn01aNAgZWdnq7q6Wps2bZIkzZkzR2vXrtXChQv11FNP6W9/+5u2bt2qkpISU4fQoazOhyRVVlZ6x9bW1qqyslKhoaEaOXKkv8vvFFbnpKioSBkZGXrjjTeUlJQkp9Mp6duLvyMiIowcQ0eyOh/5+fkaNGiQhg8fLunbW59XrVqlX/7yl0bq9wvTt/OY9uabb3oGDRrkCQ0N9SQmJno+/PBD7+8mTpzoycjI8Ol/9OhRjyTPnj17/Fyp/1iZk0uXLnmWLVvmGTp0qCcsLMxjt9s9v/jFLzz/+9///F94J7EyH2VlZZ4RI0Z4bDabp1+/fp6ZM2d6qqurDVTdeT744AOPpGbbd/OQkZHhmThxYrMxY8eO9YSGhnqGDBniefvtt/1ed2dpz3y01P/WW2/1e+2dxeqcTJw48Yr9A53V+fjtb3/rufPOOz3XX3+9Jzw83JOQkOBZt26dp6mpycwB+EGQxxPga+kAACCg9dhrRgAAQNdAGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGDU/wPjVaQg6LkcWgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.99987 0.07346\n"
     ]
    }
   ],
   "source": [
    "#Restart - may want to comment out first line\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 668 HDs with pop < 768895 to within 1318\n",
      "working on squaring up HD 1460 time is now 0\n",
      "working on squaring up HD 6013 time is now 31\n",
      "working on squaring up HD 7469 time is now 375\n",
      "working on squaring up HD 10911 time is now 773\n",
      "working on squaring up HD 11631 time is now 1082\n",
      "working on squaring up HD 12278 time is now 1210\n",
      "working on squaring up HD 12870 time is now 1280\n",
      "We have addressed underpop in a total of 668 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.99987 0.07346\n",
      "amped unit avg and sd usage are 1.00982 0.07685\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is {7192, 7193}\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 496 overPopped HDs to within 1318\n",
      "working on squaring down HD 1530 time is now 0\n",
      "working on squaring down HD 5702 time is now 22\n",
      "working on squaring down HD 6522 time is now 42\n",
      "working on squaring down HD 6738 time is now 66\n",
      "working on squaring down HD 7449 time is now 98\n",
      "working on squaring down HD 8342 time is now 134\n",
      "working on squaring down HD 11577 time is now 177\n",
      "working on squaring down HD 12532 time is now 260\n",
      "working on squaring down HD 13490 time is now 305\n",
      "We have addressed overpop in a total of 496 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = barredJettisonSet  #new 4/10/24 for MN, ban CCB jettisoning from ALL HD's, not just near ones\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b02c7bca-1eaa-445b-bd8f-97c161794ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0000000000000002\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00982 0.07685\n",
      "shed unit avg and sd usage are 1.00084 0.07477\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 1444 out of 14191\n",
      "working on HD 2398 out of 14191\n",
      "working on HD 3492 out of 14191\n",
      "working on HD 4041 out of 14191\n",
      "working on HD 5618 out of 14191\n",
      "working on HD 5924 out of 14191\n",
      "working on HD 6256 out of 14191\n",
      "working on HD 6591 out of 14191\n",
      "working on HD 6911 out of 14191\n",
      "working on HD 7260 out of 14191\n",
      "working on HD 7625 out of 14191\n",
      "working on HD 7938 out of 14191\n",
      "working on HD 8249 out of 14191\n",
      "working on HD 10160 out of 14191\n",
      "working on HD 10473 out of 14191\n",
      "working on HD 10785 out of 14191\n",
      "working on HD 11093 out of 14191\n",
      "working on HD 11404 out of 14191\n",
      "working on HD 11710 out of 14191\n",
      "working on HD 12016 out of 14191\n",
      "working on HD 12320 out of 14191\n",
      "working on HD 12631 out of 14191\n",
      "working on HD 12940 out of 14191\n",
      "working on HD 13502 out of 14191\n",
      "working on HD 13819 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 668\n",
      "overpop contigFail, enclaveFail = 0 0 out of 496\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 7192 with pop 495747.0 now has use 0.93112\n",
      "CCB unit 7193 with pop 52630.0 now has use 0.99602\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14191 HDs, non-empty HDs 7263 7194 units\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, \"HDs, non-empty HDs\",len(popHDlist), nUnits,\"units\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d27207a-8e8d-40f2-a624-62ea7ba21920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 14191 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)  #14191\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "60a24874-126f-4059-baa5-c032c623167b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv ./state_map_files/NYlowerunitTopologies_01May24.csv\n"
     ]
    }
   ],
   "source": [
    "#restart only\n",
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "borderUnits = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitVTDlist = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "535c9c39-2400-4f8d-b3bb-b505bd51f485",
   "metadata": {},
   "outputs": [],
   "source": [
    "nDistricts = 26 - 10 - 1  #NYlower, cold restart only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology -- or read in via next block\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/NYlower14191opt3ContigGoodPop.csv ./2024state_HD_output/NYlower14191opt3ContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.999999999999737\n",
      "I will now renormalize\n",
      "normalized weight is now 1.0\n",
      "I read in 14191 HD lists of units\n",
      "orig unit avg and sd usage are 1.00084 0.07477\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology -- or read in via next block\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigGoodPop.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "working avg precinct-to-HDcP distance for HD 6400\n",
      "working avg precinct-to-HDcP distance for HD 7200\n",
      "working avg precinct-to-HDcP distance for HD 8000\n",
      "working avg precinct-to-HDcP distance for HD 8800\n",
      "working avg precinct-to-HDcP distance for HD 9600\n",
      "working avg precinct-to-HDcP distance for HD 10400\n",
      "working avg precinct-to-HDcP distance for HD 11200\n",
      "working avg precinct-to-HDcP distance for HD 12000\n",
      "working avg precinct-to-HDcP distance for HD 12800\n",
      "working avg precinct-to-HDcP distance for HD 13600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking for discontiguities for HD 1500\n",
      "checking for discontiguities for HD 3000\n",
      "checking for discontiguities for HD 3500\n",
      "checking for discontiguities for HD 6000\n",
      "checking for discontiguities for HD 6500\n",
      "checking for discontiguities for HD 7500\n",
      "checking for discontiguities for HD 8000\n",
      "checking for discontiguities for HD 8500\n",
      "checking for discontiguities for HD 10500\n",
      "checking for discontiguities for HD 11000\n",
      "checking for discontiguities for HD 11500\n",
      "checking for discontiguities for HD 12000\n",
      "checking for discontiguities for HD 12500\n",
      "checking for discontiguities for HD 13000\n",
      "out of 7263 total HDs, there were 7263 0 0 0 HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"checking for discontiguities for HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d862cb95-8559-4bd1-baf8-42e7c12c32be",
   "metadata": {},
   "outputs": [],
   "source": [
    "allUnits = allUnits.to_list()  #needed if we pulled in the topology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "7192 0.93112 495747.0\n",
      "7193 0.99602 52630.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I believe BELOW UNDERUSED is faster now that we subbed wontEnclave for enclaveCheck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 1043 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 120\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1631 units out of 7194 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38848\n",
      "current avg and SD of unit usage are 1.00084 0.07477 . Now trying to increase up to 120 units' underusage\n",
      "Couldn't find any HDs adjacent to underused unit 1187\n",
      "all done trying to boost usage of unit 1187 final usage = 0.70377 16 sec elapsed 0 0 worked, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00084 0.07477\n",
      "all done trying to boost usage of unit 476 final usage = 0.97014 854 sec elapsed 149 0 worked, failed patches, couldn't start= 111\n",
      "current avg and SD of usage are 1.00085 0.07331\n",
      "all done trying to boost usage of unit 2852 final usage = 0.97068 1985 sec elapsed 163 2 worked, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00085 0.07196\n",
      "all done trying to boost usage of unit 812 final usage = 0.91527 2695 sec elapsed 117 1 worked, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00088 0.07017\n",
      "all done trying to boost usage of unit 94 final usage = 0.86323 3336 sec elapsed 65 0 worked, failed patches, couldn't start= 82\n",
      "current avg and SD of usage are 1.00087 0.06893\n",
      "all done trying to boost usage of unit 1315 final usage = 0.95028 4417 sec elapsed 116 2 worked, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00087 0.0675\n",
      "all done trying to boost usage of unit 2662 final usage = 0.97148 5731 sec elapsed 130 0 worked, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00088 0.06685\n",
      "all done trying to boost usage of unit 810 final usage = 0.83736 6541 sec elapsed 45 0 worked, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00088 0.06624\n",
      "all done trying to boost usage of unit 1500 final usage = 0.82778 7349 sec elapsed 41 2 worked, failed patches, couldn't start= 55\n",
      "current avg and SD of usage are 1.00088 0.06579\n",
      "begin usage increase for unit 2661 with usage 0.75874 . Sec, total UU units tried = 7349 10\n",
      "all done trying to boost usage of unit 2661 final usage = 0.97155 9014 sec elapsed 126 0 worked, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00087 0.0654\n",
      "all done trying to boost usage of unit 1504 final usage = 0.9484 10922 sec elapsed 106 5 worked, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 1.00086 0.0645\n",
      "all done trying to boost usage of unit 1535 final usage = 0.9064 11949 sec elapsed 69 4 worked, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 1.00087 0.06373\n",
      "all done trying to boost usage of unit 785 final usage = 0.91007 12884 sec elapsed 70 0 worked, failed patches, couldn't start= 41\n",
      "current avg and SD of usage are 1.00086 0.06319\n",
      "all done trying to boost usage of unit 1562 final usage = 0.95224 14654 sec elapsed 94 4 worked, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00086 0.0626\n",
      "all done trying to boost usage of unit 1594 final usage = 0.9232 16033 sec elapsed 80 2 worked, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00087 0.06193\n",
      "all done trying to boost usage of unit 1513 final usage = 0.90269 16747 sec elapsed 57 0 worked, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00087 0.06152\n",
      "all done trying to boost usage of unit 45 final usage = 0.95962 18926 sec elapsed 101 2 worked, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00087 0.06097\n",
      "all done trying to boost usage of unit 532 final usage = 0.85071 19034 sec elapsed 17 0 worked, failed patches, couldn't start= 69\n",
      "current avg and SD of usage are 1.00087 0.06065\n",
      "all done trying to boost usage of unit 3146 final usage = 0.97159 20712 sec elapsed 102 0 worked, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00087 0.06035\n",
      "begin usage increase for unit 636 with usage 0.79931 . Sec, total UU units tried = 20712 20\n",
      "all done trying to boost usage of unit 636 final usage = 0.82611 21023 sec elapsed 13 0 worked, failed patches, couldn't start= 93\n",
      "current avg and SD of usage are 1.00087 0.06024\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[59], line 141\u001b[0m\n\u001b[0;32m    139\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m excess \u001b[38;5;241m-\u001b[39m unitPop[shedU] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mmaxGap:\n\u001b[1;32m--> 141\u001b[0m     contig,cContig, __, ___ \u001b[38;5;241m=\u001b[39m \u001b[43menclaveCheck\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrySet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdifference\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mshedU\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    142\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m contig \u001b[38;5;129;01mand\u001b[39;00m cContig:\n\u001b[0;32m    143\u001b[0m         notYetPicked \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[27], line 722\u001b[0m, in \u001b[0;36menclaveCheck\u001b[1;34m(UNITLIST, UNITNBRS, maxLoops)\u001b[0m\n\u001b[0;32m    719\u001b[0m ADJLIST \u001b[38;5;241m=\u001b[39m  get2nbrs(UNITLIST, UNITNBRS)  \u001b[38;5;66;03m#in case direct adjoiners are only queen-adjacent\u001b[39;00m\n\u001b[0;32m    720\u001b[0m \u001b[38;5;66;03m#adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\u001b[39;00m\n\u001b[0;32m    721\u001b[0m \u001b[38;5;66;03m#BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\u001b[39;00m\n\u001b[1;32m--> 722\u001b[0m BDRYLIST \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m (\u001b[38;5;28mset\u001b[39m(\u001b[43mget2nbrs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mADJLIST\u001b[49m\u001b[43m,\u001b[49m\u001b[43mUNITNBRS\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mintersection(UNITSET) )  \u001b[38;5;66;03m#in case inHD boundary is only queen-adjacent\u001b[39;00m\n\u001b[0;32m    723\u001b[0m noEnclave, enclaveList \u001b[38;5;241m=\u001b[39m   isContiguous(ADJLIST, UNITNBRS)  \n\u001b[0;32m    724\u001b[0m unbroken, smallPieceList \u001b[38;5;241m=\u001b[39m isContiguous(BDRYLIST, UNITNBRS)\n",
      "Cell \u001b[1;32mIn[27], line 656\u001b[0m, in \u001b[0;36mget2nbrs\u001b[1;34m(ULIST, UNITNBRS)\u001b[0m\n\u001b[0;32m    652\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget2nbrs\u001b[39m(ULIST, UNITNBRS):\n\u001b[0;32m    653\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    654\u001b[0m \u001b[38;5;124;03m    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\u001b[39;00m\n\u001b[0;32m    655\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 656\u001b[0m     firstList \u001b[38;5;241m=\u001b[39m  \u001b[43mgetAdjoiners\u001b[49m\u001b[43m(\u001b[49m\u001b[43mULIST\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mUNITNBRS\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    657\u001b[0m     secondList \u001b[38;5;241m=\u001b[39m getAdjoiners(firstList, UNITNBRS)\n\u001b[0;32m    658\u001b[0m     twoLevelList \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m( \u001b[38;5;28mset\u001b[39m(firstList \u001b[38;5;241m+\u001b[39m secondList)\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mset\u001b[39m(ULIST) ) )\n",
      "Cell \u001b[1;32mIn[27], line 648\u001b[0m, in \u001b[0;36mgetAdjoiners\u001b[1;34m(UNITLIST__, UNITNBRS)\u001b[0m\n\u001b[0;32m    646\u001b[0m allNbrs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m()\n\u001b[0;32m    647\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m U \u001b[38;5;129;01min\u001b[39;00m UNITLIST__:\n\u001b[1;32m--> 648\u001b[0m     allNbrs \u001b[38;5;241m=\u001b[39m allNbrs \u001b[38;5;241m+\u001b[39m UNITNBRS[U]\n\u001b[0;32m    649\u001b[0m adjoiners \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m( \u001b[38;5;28mset\u001b[39m(allNbrs)\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mset\u001b[39m(UNITLIST__)) )\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m adjoiners\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most underuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to boost usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"worked, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "97e8abcd-5991-4f55-98c9-99dc9578b8c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1187 0 0.7037747357751941 [1187, 618, 812, 1505]\n",
      "1187 0.7037747357751941 1402.0\n",
      "1187 has neighbors [618, 812, 1505]\n",
      "618 0.7037747357751941 981.0\n",
      "618 has neighbors [812, 1187, 1503, 1505]\n",
      "812 0.7037747357751941 727.0\n",
      "812 has neighbors [618, 1187, 1503, 1505]\n",
      "1505 0.7037747357751941 1228.0\n",
      "1505 has neighbors [618, 812, 1187, 1503]\n",
      "1503 1386.0 0.7037747357751941\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(UUU, len(uuuDists), unitUse[UUU], UUUc)\n",
    "for u in UUUc:\n",
    "    print(u, unitUse[u], unitPop[u])\n",
    "    plotPoly(unitCP[u].buffer(0.01))\n",
    "    print(u,\"has neighbors\",unitNbrs[u])\n",
    "print(1503, unitPop[1503], unitUse[1503])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2377b41-61df-4f0d-9d87-a0557362328d",
   "metadata": {},
   "outputs": [],
   "source": [
    ".99959 0.09151  8:44 0.08996"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00084 1.00087 and their SDs are 0.07477 0.06002\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 1500 out of 14191\n",
      "working on HD 2400 out of 14191\n",
      "working on HD 2700 out of 14191\n",
      "working on HD 3000 out of 14191\n",
      "working on HD 3600 out of 14191\n",
      "working on HD 4200 out of 14191\n",
      "working on HD 5700 out of 14191\n",
      "working on HD 6000 out of 14191\n",
      "working on HD 6900 out of 14191\n",
      "working on HD 7200 out of 14191\n",
      "working on HD 7500 out of 14191\n",
      "working on HD 7800 out of 14191\n",
      "working on HD 8100 out of 14191\n",
      "working on HD 8400 out of 14191\n",
      "working on HD 10200 out of 14191\n",
      "working on HD 10500 out of 14191\n",
      "working on HD 10800 out of 14191\n",
      "working on HD 11100 out of 14191\n",
      "working on HD 11400 out of 14191\n",
      "working on HD 11700 out of 14191\n",
      "working on HD 12000 out of 14191\n",
      "working on HD 12300 out of 14191\n",
      "working on HD 12600 out of 14191\n",
      "working on HD 12900 out of 14191\n",
      "working on HD 13800 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"21unitUnderPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.08\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 1992 units out of 7194 with usage above 1.03\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38848\n",
      "current avg and SD of unit usage are 1.00087 0.06002 . Now trying to reduce up to 1992 units' overusage\n",
      "starting to reduce usage for unit 3594 with usage 1.31208 . Total units tried = 1\n",
      "try to drop unit 3594 from 49 th HD.  usage down to 1.2037452076956818\n",
      "try to drop unit 3594 from 98 th HD.  usage down to 1.1250957945834272\n",
      "try to drop unit 3594 from 147 th HD.  usage down to 1.064967955166744\n",
      "all done trying to reduce usage of unit 3594 final usage = 1.02989 281 sec elapsed 119 0 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.00084 0.0563\n",
      "starting to reduce usage for unit 2280 with usage 1.20423 . Total units tried = 2\n",
      "try to drop unit 2280 from 59 th HD.  usage down to 1.1672016342075968\n",
      "try to drop unit 2280 from 118 th HD.  usage down to 1.1257460131943116\n",
      "try to drop unit 2280 from 177 th HD.  usage down to 1.1025145391457156\n",
      "try to drop unit 2280 from 236 th HD.  usage down to 1.0897226145947638\n",
      "try to drop unit 2280 from 295 th HD.  usage down to 1.067544365939492\n",
      "all done trying to reduce usage of unit 2280 final usage = 1.06505 380 sec elapsed 70 5 successful, failed patches, couldn't start= 224\n",
      "current avg and SD of usage are 1.00083 0.05503\n",
      "starting to reduce usage for unit 3505 with usage 1.19054 . Total units tried = 3\n",
      "try to drop unit 3505 from 43 th HD.  usage down to 1.1292584812540956\n",
      "try to drop unit 3505 from 86 th HD.  usage down to 1.0583962407320988\n",
      "all done trying to reduce usage of unit 3505 final usage = 1.02743 423 sec elapsed 72 0 successful, failed patches, couldn't start= 31\n",
      "current avg and SD of usage are 1.00081 0.05342\n",
      "starting to reduce usage for unit 3434 with usage 1.15992 . Total units tried = 4\n",
      "try to drop unit 3434 from 46 th HD.  usage down to 1.0801495940575454\n",
      "all done trying to reduce usage of unit 3434 final usage = 1.02852 488 sec elapsed 64 0 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00078 0.05239\n",
      "starting to reduce usage for unit 5529 with usage 1.13468 . Total units tried = 5\n",
      "try to drop unit 5529 from 22 th HD.  usage down to 1.0809581827458041\n",
      "all done trying to reduce usage of unit 5529 final usage = 1.02845 502 sec elapsed 39 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00078 0.05151\n",
      "starting to reduce usage for unit 3421 with usage 1.12813 . Total units tried = 6\n",
      "try to drop unit 3421 from 35 th HD.  usage down to 1.0523266753235967\n",
      "all done trying to reduce usage of unit 3421 final usage = 1.02869 531 sec elapsed 41 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00076 0.05091\n",
      "starting to reduce usage for unit 1725 with usage 1.12364 . Total units tried = 7\n",
      "try to drop unit 1725 from 24 th HD.  usage down to 1.0662941436204119\n",
      "all done trying to reduce usage of unit 1725 final usage = 1.02942 548 sec elapsed 39 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00075 0.05013\n",
      "starting to reduce usage for unit 3148 with usage 1.11661 . Total units tried = 8\n",
      "try to drop unit 3148 from 35 th HD.  usage down to 1.0484279387820332\n",
      "all done trying to reduce usage of unit 3148 final usage = 1.02916 569 sec elapsed 42 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00075 0.04955\n",
      "starting to reduce usage for unit 2670 with usage 1.11788 . Total units tried = 9\n",
      "try to drop unit 2670 from 28 th HD.  usage down to 1.0520575749481056\n",
      "all done trying to reduce usage of unit 2670 final usage = 1.02834 638 sec elapsed 34 1 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00074 0.0488\n",
      "starting to reduce usage for unit 7 with usage 1.10773 . Total units tried = 10\n",
      "try to drop unit 7 from 13 th HD.  usage down to 1.0872337580112283\n",
      "try to drop unit 7 from 26 th HD.  usage down to 1.0622009852764933\n",
      "try to drop unit 7 from 39 th HD.  usage down to 1.0428502219069693\n",
      "all done trying to reduce usage of unit 7 final usage = 1.02879 772 sec elapsed 36 4 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00073 0.04824\n",
      "starting to reduce usage for unit 4406 with usage 1.1017 . Total units tried = 11\n",
      "all done trying to reduce usage of unit 4406 final usage = 1.02923 797 sec elapsed 32 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00072 0.04767\n",
      "starting to reduce usage for unit 4358 with usage 1.10254 . Total units tried = 12\n",
      "try to drop unit 4358 from 24 th HD.  usage down to 1.0801907960289887\n",
      "try to drop unit 4358 from 48 th HD.  usage down to 1.048197465254754\n",
      "all done trying to reduce usage of unit 4358 final usage = 1.02935 824 sec elapsed 38 0 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00072 0.04726\n",
      "starting to reduce usage for unit 6635 with usage 1.0948 . Total units tried = 13\n",
      "try to drop unit 6635 from 58 th HD.  usage down to 1.08230754730852\n",
      "all done trying to reduce usage of unit 6635 final usage = 1.02902 876 sec elapsed 34 0 successful, failed patches, couldn't start= 80\n",
      "current avg and SD of usage are 1.00072 0.04675\n",
      "starting to reduce usage for unit 768 with usage 1.0922 . Total units tried = 14\n",
      "try to drop unit 768 from 35 th HD.  usage down to 1.039832177503591\n",
      "all done trying to reduce usage of unit 768 final usage = 1.02896 975 sec elapsed 38 2 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00072 0.04634\n",
      "starting to reduce usage for unit 5422 with usage 1.09043 . Total units tried = 15\n",
      "all done trying to reduce usage of unit 5422 final usage = 1.01766 1050 sec elapsed 27 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00071 0.04598\n",
      "starting to reduce usage for unit 2913 with usage 1.08832 . Total units tried = 16\n",
      "try to drop unit 2913 from 48 th HD.  usage down to 1.039148482291055\n",
      "all done trying to reduce usage of unit 2913 final usage = 1.02947 1079 sec elapsed 19 0 successful, failed patches, couldn't start= 34\n",
      "current avg and SD of usage are 1.00071 0.04558\n",
      "starting to reduce usage for unit 2307 with usage 1.0851 . Total units tried = 17\n",
      "try to drop unit 2307 from 22 th HD.  usage down to 1.058303536296505\n",
      "all done trying to reduce usage of unit 2307 final usage = 1.0296 1098 sec elapsed 26 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.0007 0.04522\n",
      "starting to reduce usage for unit 5889 with usage 1.07879 . Total units tried = 18\n",
      "all done trying to reduce usage of unit 5889 final usage = 1.02849 1102 sec elapsed 21 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.0007 0.04479\n",
      "starting to reduce usage for unit 632 with usage 1.08042 . Total units tried = 19\n",
      "try to drop unit 632 from 30 th HD.  usage down to 1.051754997970829\n",
      "all done trying to reduce usage of unit 632 final usage = 1.02981 1319 sec elapsed 24 7 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.0007 0.04444\n",
      "starting to reduce usage for unit 4901 with usage 1.07344 . Total units tried = 20\n",
      "all done trying to reduce usage of unit 4901 final usage = 1.02972 1340 sec elapsed 24 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0007 0.04423\n",
      "starting to reduce usage for unit 1948 with usage 1.07368 . Total units tried = 21\n",
      "try to drop unit 1948 from 20 th HD.  usage down to 1.0390583529786153\n",
      "all done trying to reduce usage of unit 1948 final usage = 1.02801 1352 sec elapsed 21 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00069 0.04399\n",
      "starting to reduce usage for unit 1889 with usage 1.07308 . Total units tried = 22\n",
      "all done trying to reduce usage of unit 1889 final usage = 1.02817 1361 sec elapsed 19 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00069 0.04374\n",
      "starting to reduce usage for unit 6830 with usage 1.07271 . Total units tried = 23\n",
      "all done trying to reduce usage of unit 6830 final usage = 1.02102 1402 sec elapsed 9 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00069 0.0434\n",
      "starting to reduce usage for unit 736 with usage 1.07193 . Total units tried = 24\n",
      "try to drop unit 736 from 18 th HD.  usage down to 1.039090542018877\n",
      "all done trying to reduce usage of unit 736 final usage = 1.02758 1576 sec elapsed 21 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00069 0.04307\n",
      "starting to reduce usage for unit 3051 with usage 1.07708 . Total units tried = 25\n",
      "all done trying to reduce usage of unit 3051 final usage = 1.02715 1619 sec elapsed 21 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00068 0.04278\n",
      "starting to reduce usage for unit 2648 with usage 1.08079 . Total units tried = 26\n",
      "try to drop unit 2648 from 19 th HD.  usage down to 1.0314707524372781\n",
      "all done trying to reduce usage of unit 2648 final usage = 1.02794 1647 sec elapsed 18 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00067 0.04234\n",
      "starting to reduce usage for unit 3704 with usage 1.06796 . Total units tried = 27\n",
      "all done trying to reduce usage of unit 3704 final usage = 1.02746 1672 sec elapsed 19 0 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00067 0.04209\n",
      "starting to reduce usage for unit 1339 with usage 1.06726 . Total units tried = 28\n",
      "try to drop unit 1339 from 15 th HD.  usage down to 1.0415884115335328\n",
      "all done trying to reduce usage of unit 1339 final usage = 1.02916 1715 sec elapsed 18 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00067 0.04179\n",
      "starting to reduce usage for unit 6290 with usage 1.06651 . Total units tried = 29\n",
      "try to drop unit 6290 from 17 th HD.  usage down to 1.032733850372223\n",
      "all done trying to reduce usage of unit 6290 final usage = 1.02904 1728 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00067 0.04155\n",
      "starting to reduce usage for unit 3379 with usage 1.06748 . Total units tried = 30\n",
      "all done trying to reduce usage of unit 3379 final usage = 1.02942 1769 sec elapsed 13 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00066 0.04137\n",
      "starting to reduce usage for unit 2000 with usage 1.06505 . Total units tried = 31\n",
      "try to drop unit 2000 from 50 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2000 from 100 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2000 from 150 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2000 from 200 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2000 from 250 th HD.  usage down to 1.0650477839863666\n",
      "all done trying to reduce usage of unit 2000 final usage = 1.06505 1777 sec elapsed 0 0 successful, failed patches, couldn't start= 254\n",
      "current avg and SD of usage are 1.00066 0.04137\n",
      "starting to reduce usage for unit 2001 with usage 1.06505 . Total units tried = 32\n",
      "try to drop unit 2001 from 50 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2001 from 100 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2001 from 150 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2001 from 200 th HD.  usage down to 1.0650477839863666\n",
      "try to drop unit 2001 from 250 th HD.  usage down to 1.0650477839863666\n",
      "all done trying to reduce usage of unit 2001 final usage = 1.06505 1785 sec elapsed 0 0 successful, failed patches, couldn't start= 254\n",
      "current avg and SD of usage are 1.00066 0.04137\n",
      "starting to reduce usage for unit 4337 with usage 1.06352 . Total units tried = 33\n",
      "all done trying to reduce usage of unit 4337 final usage = 1.0291 1810 sec elapsed 14 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00066 0.0412\n",
      "starting to reduce usage for unit 1646 with usage 1.06243 . Total units tried = 34\n",
      "all done trying to reduce usage of unit 1646 final usage = 1.02947 1820 sec elapsed 10 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00066 0.04101\n",
      "starting to reduce usage for unit 1283 with usage 1.06218 . Total units tried = 35\n",
      "all done trying to reduce usage of unit 1283 final usage = 1.02885 1916 sec elapsed 14 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00066 0.0408\n",
      "starting to reduce usage for unit 710 with usage 1.06215 . Total units tried = 36\n",
      "try to drop unit 710 from 15 th HD.  usage down to 1.03763173471974\n",
      "all done trying to reduce usage of unit 710 final usage = 1.02837 2038 sec elapsed 18 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00066 0.04054\n",
      "starting to reduce usage for unit 2752 with usage 1.06057 . Total units tried = 37\n",
      "all done trying to reduce usage of unit 2752 final usage = 1.02968 2052 sec elapsed 12 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00065 0.04041\n",
      "starting to reduce usage for unit 460 with usage 1.05889 . Total units tried = 38\n",
      "try to drop unit 460 from 34 th HD.  usage down to 1.0357055425579524\n",
      "all done trying to reduce usage of unit 460 final usage = 1.02928 2137 sec elapsed 12 0 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 1.00066 0.04012\n",
      "starting to reduce usage for unit 5002 with usage 1.05866 . Total units tried = 39\n",
      "try to drop unit 5002 from 19 th HD.  usage down to 1.033142007401213\n",
      "all done trying to reduce usage of unit 5002 final usage = 1.0282 2148 sec elapsed 14 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00066 0.03993\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA\n",
    "maxSD = 0.04 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90621-e7fc-4891-a5cd-69033c3c48e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy one of two above blocks and run again if needed (n/a for NC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units, used more than 1.06\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'unitGeom' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[63], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m u \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(nUnits):\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m unitUse[u] \u001b[38;5;241m>\u001b[39m maxPlot:\n\u001b[1;32m----> 5\u001b[0m         plotPoly(\u001b[43munitGeom\u001b[49m[u])\n\u001b[0;32m      6\u001b[0m         plotCenter(\u001b[38;5;28mround\u001b[39m(unitUse[u],\u001b[38;5;241m2\u001b[39m),unitGeom[u])\n\u001b[0;32m      7\u001b[0m plotPoly(MAP,\u001b[38;5;241m0.2\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'unitGeom' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "maxPlot = 1.06\n",
    "print(\"overused units, used more than\", maxPlot)\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "minPlot = 0.94\n",
    "print(\"Now underused units, used less than\",minPlot)\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00084 1.00066 and their SDs are 0.07477 0.03993\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 1500 out of 14191\n",
      "working on HD 2400 out of 14191\n",
      "working on HD 2700 out of 14191\n",
      "working on HD 3000 out of 14191\n",
      "working on HD 3600 out of 14191\n",
      "working on HD 4200 out of 14191\n",
      "working on HD 5700 out of 14191\n",
      "working on HD 6000 out of 14191\n",
      "working on HD 6900 out of 14191\n",
      "working on HD 7200 out of 14191\n",
      "working on HD 7500 out of 14191\n",
      "working on HD 7800 out of 14191\n",
      "working on HD 8100 out of 14191\n",
      "working on HD 8400 out of 14191\n",
      "working on HD 10200 out of 14191\n",
      "working on HD 10500 out of 14191\n",
      "working on HD 10800 out of 14191\n",
      "working on HD 11100 out of 14191\n",
      "working on HD 11400 out of 14191\n",
      "working on HD 11700 out of 14191\n",
      "working on HD 12000 out of 14191\n",
      "working on HD 12300 out of 14191\n",
      "working on HD 12600 out of 14191\n",
      "working on HD 12900 out of 14191\n",
      "working on HD 13800 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "working on full or partial VTD master list for unit 6000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "#NOTE 2 - this probably incorrectly misses surrounded if we read in the topologies rather than generated in this kernel,\n",
    "#   so go back and recreate the surrounds before running this\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14191 14191 14191 14191 14191 14191\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 14191\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3fa86ae7-c24c-4053-9903-265de56c5c31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming the current HDvtdList represents lower NY, with 15 districts\n",
      "all done combining HD lists into a master list.  Total weight = 0.9999727638277377\n"
     ]
    }
   ],
   "source": [
    "#SPECIAL FOR NY -- COMBINE LOWER AND UPPER.  ADD IN THE WHITE PLAINS DISTRICT.  REWEIGHT THEM.\n",
    "nLower, nUpper, nCut = 15, 10, 1\n",
    "nTotal = nLower + nUpper + nCut\n",
    "PATH = \"./2024state_HD_output/\"\n",
    "lowerFile,upperFile,cutFile = \"NYlower14191patchedVTDs.csv\", \"NYupper14191patchedVTDs.csv\", \"NYlowerwholeDistrictCuts.csv\"\n",
    "print(\"Assuming the current HDvtdList represents lower NY, with\",nLower,\"districts\")\n",
    "upperDF = pd.read_csv(PATH+upperFile)\n",
    "cutDF =   pd.read_csv(PATH+cutFile)\n",
    "lowerDF = pd.read_csv(PATH+lowerFile)\n",
    "hdCPx, hdCPy = lowerDF[\"centroid x\"], lowerDF[\"centroid y\"]  #these are the same for upper and lower\n",
    "lowerListString = lowerDF[\"HDvtdList\"]\n",
    "lowerPartialString, upperPartialString = lowerDF[\"partials\"], upperDF[\"partials\"]\n",
    "lowerFracString, upperFracString = lowerDF[\"HDvtdFrac\"], upperDF[\"HDvtdFrac\"]\n",
    "upperListString = upperDF[\"HDvtdList\"]\n",
    "cutColumn = cutDF[\"vtdNo\"]\n",
    "cutBigList = list()\n",
    "for v in cutColumn:\n",
    "    cutBigList.append(v)\n",
    "nHDs = len(lowerListString)\n",
    "lowerVTDlist =     [ast.literal_eval(lowerListString[t]) for t in range(nHDs)]\n",
    "upperVTDlist =     [ast.literal_eval(upperListString[t]) for t in range(nHDs)]\n",
    "lowerPartialList = [ast.literal_eval(lowerPartialString[t]) for t in range(nHDs)]\n",
    "upperPartialList = [ast.literal_eval(upperPartialString[t]) for t in range(nHDs)]\n",
    "lowerFracList =    [ast.literal_eval(lowerFracString[t]) for t in range(nHDs)]\n",
    "upperFracList =    [ast.literal_eval(upperFracString[t]) for t in range(nHDs)]\n",
    "lowerWt, upperWt = lowerDF[\"HDweight\"], upperDF[\"HDweight\"]\n",
    "masterList, masterWt = [list() for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "masterPartials, masterFracs,  = [list() for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "weWroteCutList = False\n",
    "for t in range(nHDs):\n",
    "    if len(lowerVTDlist[t]) > 0:\n",
    "        masterList[t] =     lowerVTDlist[t].copy()\n",
    "        masterWt[t]   =     lowerWt[t] * nLower / float(nTotal)\n",
    "        masterPartials[t] = lowerPartialList[t].copy()\n",
    "        masterFracs[t]    = lowerFracList[t].copy()\n",
    "    \n",
    "    if len(upperVTDlist[t]) > 0:\n",
    "        if masterWt[t] > 0:\n",
    "            raise Exception(\"ERROR! HD\",t,\"populated in both upper and lower VTD list rows\")\n",
    "        else:\n",
    "            masterList[t] =     upperVTDlist[t].copy()\n",
    "            masterWt[t]   =     upperWt[t] * nUpper / float(nTotal)\n",
    "            masterPartials[t] = upperPartialList[t].copy()\n",
    "            masterFracs[t]    = upperFracList[t].copy()\n",
    "\n",
    "    if t in cutBigList:\n",
    "        if masterWt[t] > 0:\n",
    "            raise Exception(\"ERROR! vtd\",t,\"is in cut list but also was filled in as a lower or upper HD\")\n",
    "        else:\n",
    "            if not weWroteCutList:\n",
    "                masterList[t] = cutBigList.copy()\n",
    "                masterWt[t] = nCut / float(nTotal)  #the cut district has no partials       \n",
    "                weWroteCutList = True\n",
    "print(\"all done combining HD lists into a master list.  Total weight =\",np.sum(masterWt) )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "95801f74-69ff-4ff9-aa07-aa3c8948e291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing special combination for NY\n",
      "summing pop for HD 0\n",
      "summing pop for HD 3000\n",
      "summing pop for HD 6000\n",
      "summing pop for HD 9000\n",
      "summing pop for HD 12000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"continuing special combination for NY\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%3000 == 0:\n",
    "        print(\"summing pop for HD\",t)\n",
    "    HDvPop[t] = np.sum([tractPop[tt] for tt in masterList[t] ])\n",
    "    for i,tt in enumerate(masterPartials[t]):\n",
    "        HDvPop[t] += masterFracs[t][i] * tractPop[tt]\n",
    "popPops = list()\n",
    "for t in range(nHDs):\n",
    "    if HDvPop[t] > 0:\n",
    "        popPops.append(HDvPop[t])\n",
    "plt.hist(popPops, label=\"HD pops\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "HDfullVTDlist, HDweight = masterList.copy(), masterWt.copy()\n",
    "HDpartialVTDlist, HDvtdFrac = masterPartials.copy(), masterFracs.copy()\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = \"NYcombined\"+str(int(nHDs))+\"PatchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "13 1 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")  #skip for NY\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999727638277377\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE = \"NY\"  #reset for below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for NY\n",
      "In year/election 2020 Dem and GOP total votes were 5209888 3245516 for a stateGOP of 0.38384\n",
      "There are a total of 14191  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 4524696 2815519 for a stateGOP of 0.38357\n",
      "There are a total of 14191  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7abca973-b2aa-41ef-8b8c-41475bfad07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "censusGEOID20 = str(tractPopFile['GEOID20'])\n",
    "fred = str(mergedDF['GEOID20'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for NY\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv NYcombined14191PatchedWithCutsVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 14191 drawn districts.  This state has 26 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 0.99997\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = [ str( tractPopFile['GEOID20'][i]) for i in range(len(tractPopFile['GEOID20'])) ] #force strings to match\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "mergedGEO = [str(mergedDF['GEOID20'][i]) for i in range(len(mergedDF['GEOID20'])) ]  #force strings to match\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedGEO, \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:  #< or >\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"d\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "working on drawn district no 8000\n",
      "working on drawn district no 9000\n",
      "working on drawn district no 10000\n",
      "working on drawn district no 11000\n",
      "working on drawn district no 12000\n",
      "working on drawn district no 13000\n",
      "working on drawn district no 14000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 5209888 3245516 -1964371\n",
      "ensemble : 3210869 1995196 -1215673\n",
      "the true and ensemble state GOP leans are 0.38384 0.38324452370871614\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 14191 14191 16\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
